Assessment of Selected LTPP Material Data Tables and Development of Representative Test Tables and Development of Representative Test Tables (FHWA-RD-02-001)

Accurate and reliable information about pavement material properties is key to predicting the states of stress, strain, and displacement within the pavement structure when subjected to an external wheel and climate-related loading. Computed stress and strain are then used as critical responses that are needed for predicting distress and pavement performance. For example, Portland cement concrete (PCC) cracking is related to the PCC flexural strength, and pumping and faulting can be related to the erodibility of the underlying base/subbase material. The inclusion of accurate material-related data is, therefore, vital in research studies such as the Long-Term Pavement Performance (LTPP) study.

This report documents the state of selected material-related data elements in the LTPP material characterization program. The data were evaluated to assess completeness and quality. Recommendations are also provided regarding the suitability of the data evaluated for future research and analysis. The report also provides information on representative data tables developed as part of this study and recommended for inclusion in the LTPP database. The report is intended for all LTPP data users--from those with considerable experience to those with no familiarity with the LTPP database.

This document is distributed under the sponsorship of the Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for its contents or use thereof. This report does not constitute a standard, specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trade and manufacturers' names appear in this report only because they are considered essential to the object of the document.

4. Title and Subtitle: ASSESSMENT OF SELECTED LTPP MATERIAL DATA TABLES AND DEVELOPMENT OF REPRESENTATIVE TEST TABLES

7. Author(s): Leslie Titus-Glover, Jagannath Mallela, Y. Jane Jiang, Michael E. Ayers, and Haroon I. Shami

9. Performing Organization Name and Address: ERES Consultants, 9030 Red Branch Road, Suit 210, Columbia, MD 21045

12. Sponsoring Agency Name and Address: Office on Infrastructure Research and Development, Federal Highway Administration, 6300 Georgetown PikeMcLean, Virginia 22101-2296

13. Type of Report and Period Covered: Final Report, September 1999 to August 2001

15. Supplementary Notes: Contracting Officer's Technical Representative (COTR): Cheryl Allen Richter, P.E., HRDI-13

This report documents an evaluation of selected LTPP material data tables as of January 2000. Issues addressed include the availability, characteristics, and quality of the data in the selected tables. Anomalies in the data were identified and corrected where possible, and the "cleaned-out" data were used in developing representative data tables. Recommendations for adjustments in the current data collection process are also presented.

17. Key Words: Bias, concrete pavement, paving materials, precision, variability.

18. Distribution Statement: No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161.

Length:
inches (in) multiply by 25.4 to get millimeters (mm)
feet (ft) multiply by 0.305 to get meters (m)
yards (yd) multiply by 0.914 to get meters (m)
miles (mi) multiply by 1.61 to get kilometers (km)

Area:
square inches (in²) multiply by 645.2 to get square millimeters (mm²)
square feet (ft²) multiply by 0.093 to get square meters (m²)
square yard (yd²) multiply by 0.836 to get square meters (m²)
acres (ac) multiply by 0.405 to get hectares (ha)
square miles (mi²) multiply by 2.59 to get square kilometers (km²)

Volume:
fluid ounces (fl oz) multiply by 29.57 to get milliliters (mL)
gallons (gal) multiply by 3.785 to get liters (L)
cubic feet (ft³) multiply by 0.028 to get cubic meters (m³)
cubic yards (yd³) multiply by 0.765 to get cubic meters (m³)
NOTE: volumes greater than 1000 L shall be shown in m³

Mass:
ounces (oz) multiply by 28.35 to get grams (g)
pounds (lb) multiply by 0.454 to get kilograms (kg)
short tons - 2000 lb (T) multiply by 0.907 to get megagrams or "metric ton" (Mg or "t")

Temperature (exact degrees):
Fahrenheit (°F) multiply by 5 (F-32)/9 or (F-32)/1.8 to get Celsius (°C)

Illumination:
foot-candles (fc) multiply by 10.76 to get lux (lx)
foot-Lamberts (fl) multiply by 3.426 to get candela/m² (cd/m²)

Force and Pressure or Stress:
poundforce (lbf) multiply by 4.45 to get newtons (N)
poundforce per square inch (lbf/in²) multiply by 6.89 to get kilopascals (kPa)

Length:
millimeters (mm) multiply by 0.039 to get inches (in)
meters (m) multiply by 3.28 to get feet (ft)
meters (m) multiply by 1.09 to get yards (yd)
kilometers (km) multiply by 0.621 to get miles (mi)

Area:
square millimeters (mm²) multiply by 0.0016 to get square inches (in²)
square meters (m²) multiply by 10.764 to get square feet (ft²)
square meters (m²) multiply by 1.195 to get square yards (yd²)
hectares (ha) multiply by 2.47 to get acres (ac)
square kilometers (km²) multiply by 0.386 to get square miles (mi²)

Volume:
milliliters (mL) multiply by 0.034 to get fluid ounces (fl oz)
liters (L) multiply by 0.264 to get gallons (gal)
cubic meters (m³) multiply by 35.314 to get cubic feet (ft³)
cubic meters (m³) multiply by 1.307 to get cubic yards (yd³)

Mass:
grams (g) multiply by 0.035 to get ounces (oz)
kilograms (kg) multiply by 2.202 to get pounds (lb)
megagrams or "metric ton" (Mg or "t") multiply by 1.103 to get short tons - 2000 lb (T)

Temperature (exact degrees):
Celsius (°C) multiply by 1.8C+32 to get Fahrenheit (°F)

Illumination:
lux (lx) multiply by 0.0929 to get foot-candles (fc)
candela/m² (cd/m²) multiply by 0.2919 to get foot-Lamberts (fl)

Force and Pressure or Stress:
newtons (N) multiply by 0.225 to get poundforce (lbf)
kilopascals (kPa) multiply by 0.145 to get poundforce per square inch (lbf/in²)

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380.
(Revised March 2002)

Information about pavement material properties is required to predict states of stress, strain, and displacement within the pavement structure when subjected to an external wheel load. In both empirical and mechanistic-empirical (M-E) design systems, material properties such as elastic modulus (E), compressive strength (f_c), Poisson's ratio (mu), tensile strength (f_t), and flexural strength (M_R) are mandatory inputs for characterizing pavement systems, computing pavement critical responses (e.g., stress and strain) to applied traffic and climate-related loads, and predicting performance.

Material characterization is vital to pavement design because most of the major distresses that occur in pavements can be associated with the material properties of a component or layer of the pavement structure. For example, portland cement concrete (PCC) cracking is related to the PCC flexural strength, and pumping and faulting can be related to the erodibility of the underlying base/subbase material. Material characterization is also vital in research studies, such as the Federal Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) study, to determine the effects of material properties on pavement performance.

One of the important objectives of the LTPP program is to understand the relationship between pavement material properties and pavement performance. A better understanding of this relationship will ensure that appropriate materials are specified and used in pavement construction to provide the desired level of performance. LTPP material characterization, therefore, serves the following purposes:⁽¹⁾

As of January 2000, over 775 GPS and SPS sites have been sampled as part of the material characterization program. Sampling from these sites included the extraction of almost 14,000 cores (200 tons of bulk samples), the excavation of over 450 test pits, and the performance of over 330 in situ nuclear density tests.⁽¹⁾

Asphalt cement is obtained by the distillation of crude petroleum. At ambient temperatures, it is a black, sticky, semisolid, highly viscous material. The primary use of asphalt cement is to produce AC and asphalt-treated materials for use in the construction of flexible pavements.

The properties of the asphalt cement used in producing AC have a significant influence on the final AC properties. Some of the important AC properties influenced by asphalt cement are:

Several tests are performed on new and extracted asphalt cement materials as part of the LTPP materials characterization program.

Asphalt concrete describes a broad range of aggregate and filler materials bound together with asphalt cement. It is normally used as the wearing, binder, base, and/or subbase layers of a flexible pavement. Their properties and behavior are heavily influenced by temperature, time rate of loading, mixture proportions, and construction. This category of pavement materials includes many different subgroups--ranging from the hot mix asphalt layer to the asphalt-treated sand. Thus, there is a great deal of variability in properties, behavior, and suitability for pavement design and construction.

Portland cement is the principal strength-giving and property-controlling component of PCC. It is a hydraulic cement that gains strength as it reacts with water. Although the reaction is initially fairly rapid (approximately 40 percent occurs within the first 24 hours), it slows down considerably with the passage of time. The hydration reaction produces a finely divided, fairly porous solid between the fine and coarse aggregate particles in PCC. Portland cement properties are, thus, very important for PCC material design and research into PCC strength and durability.

Cement-treated/cementitious materials include lean concrete, lime, and other pozzolanic (chemical)-treated soils. Their properties range from materials that only slightly modify the plasticity characteristics of the original aggregate/soil material to materials having major gains in stiffness, strength, and other key engineering properties. Lime, flyash, and cement are the major types of cementing material in this category.

The major material characteristics associated with unbound base/subbase materials are related to the fact that the strength and deformation of these materials are highly influenced by the stress state (nonlinear) and in situ moisture content. As a general rule, coarse-grained materials become more stiff as the state of stress is increased. In contrast, clay materials tend to have a reduction in stiffness as the deviatoric or octahedral stress component is increased. Thus, although both categories of unbound materials are stress dependent (nonlinear), each behaves in an opposite direction as stress states are increased.

The subgrade provides the foundation of a pavement system. Subgrade characterization is, therefore, an important component of pavement design and construction. Subgrade properties such as gradation, plasticity, strength, moisture sensitivity, permeability, and frost susceptibility are important for characterizing pavement subgrade materials for pavement design.

Characterizing subgrade material variability is key for assessing the reliability of pavement designs. This is especially true for pavements constructed directly over natural subgrade with no form of modifications or treatment with lime, asphalt, or cement.

The LTPP laboratory testing process, conducted to establish material properties and characteristics, involved more than 40 test procedures, including thickness determinations, compressive strength, gradation, Atterberg limits, and resilient modulus.⁽¹⁾

The sampling and laboratory testing were conducted using Strategic Highway Research Program (SHRP) test protocols documented in the SHRP Laboratory Material Handling and Testing Guide.⁽²⁾ Most of the testing protocols were based on the American Society for Testing and Materials (ASTM) and American Association of State Highway and Transportation Officials (AASHTO) approved procedures.^{(3, 4)} Data obtained through material characterization tests are stored in the Materials module in the LTPP database, which currently consists of 76 separate tables containing various data elements obtained through testing.⁽⁵⁾

Efforts to achieve these objectives were divided into two phases. The scope of Phase I was as follows:

Phase II of the study consisted of tasks to resolve anomalies identified in Phase I to reduce excessive variability in the data and to compute representative test values and new parameters. The scope of Phase II was as follows:

This report presents the work done to improve the LTPP material data quality and to include new data elements in the database. It addresses work done in assessing and evaluating material data availability and quality of the available data. Chapter 2 of this report presents an overview of the LTPP material characterization program, with emphasis placed on the key material-related data elements being evaluated as part of this study. Chapter 3 discusses various methodologies for assessing data availability, data quality, performance of remedial action, and computing of representative test values.

Chapters 4 through 17 present a detailed summary of data availability and quality for the 13 data elements evaluated in this study. They also present detailed documentation of the procedures used in correcting anomalies, computing missing test results, and computing representative test values and other basic statistics.

Chapter 18 presents conclusions and recommendations. Issues discussed in chapter 18 include the adequacy of current LTPP material data-collection procedures, the need for computing new data elements with respect to meeting future analysis needs, and recommendations for collecting (through sampling and testing) material-related data elements that are not currently available but may be needed for future analysis efforts.

The LTPP program consists of two complementary experiments, GPS and SPS. GPS experiments are usually existing in-service pavements incorporated into the LTPP program, whereas the SPS experiments are newly constructed or rehabilitated pavements or pavements subjected to various forms of maintenance activities. GPS experiments consist of a single 152-m pavement test section, whereas SPS experiments usually consist of a series of adjacent 152-m test sections with different design and material characteristics or maintenance treatments and rehabilitation strategies. The various GPS and SPS experiments are as follows:⁽¹⁾

Figure 1 shows the typical layout of GPS and adjacent SPS test sections (projects) within an experiment. The typical SPS experiment consists of 12 test sections at a project site.

Material characterization, distress, climate, traffic, inventory, and other types of data are collected and stored in the LTPP database by four LTPP regional offices, supervised by the LTPP staff. Each regional office is responsible for data collection in a specific group of States, Provinces, and Territories.⁽¹⁾ For material characterization, the regional offices focus on:

The technical support contractor is responsible for quality assurance (QA) of all LTPP data. It is also responsible for providing the data to the public and assists the LTPP staff in ensuring that common test procedures and standards are used, so that there is consistency in the data-collection process and quality control (QC) is maintained at all times in the material characterization program. Specific procedures employed to maintain QC are discussed in the next section.

The LTPP materials characterization effort begins with field sampling and laboratory testing of the sampled paving materials. Undisturbed material samples or disturbed bulk samples are obtained by drilling, coring, or excavating test pits at designated locations within the pavement test sections. Sampling was performed using the plans and guidelines provided in the Laboratory Material Handling and Testing Operational Guide.⁽²⁾

For GPS experiments, samples were marked and shipped to the designated laboratories for testing by the LTPP regional field material sampling and field testing contractors. For SPS experiments, the samples were marked and shipped to the designated laboratories for testing by the local highway agencies or tested in the agency laboratory facilities. Conditions encountered in the field while sampling were recorded and documented to provide the laboratories with adequate background information on the materials to be tested.

The typical LTPP pavement test section consists of an area 152-m long and one lane wide. Nondestructive tests, such as Falling Weight Deflectometer (FWD) tests to obtain pavement deflection under loading and visual surveys to obtain distress data such as cracks and ruts, are conducted periodically on these sections to characterize pavement performance.¹ For this reason, paving materials for laboratory characterization were retrieved only from the beginning and the end of the designated test sections (just outside the 152-m limits). Sufficient space is left between the adjacent test sections in SPS experiments to facilitate the drilling and retrieval of paving materials for testing. Details of sampling location and dimensions of cores or test pits for the different LTPP experiments are provided in the Laboratory Material Handling and Testing Operational Guide.⁽²⁾

¹ For a complete history of and information regarding the frequency of NDT and visual distress surveys, refer to appropriate FHWA reference manuals and reports.

After retrieving sample materials from coring or test pits, the sample materials were marked and labeled for easy identification before shipping to the testing laboratory. As a minimum, the following information was included on tags and labels:

Laboratory tests were performed on several paving materials, including AC, extracted aggregate from the AC, treated base and subbase, untreated base and subbase, subgrade, and PCC materials. The tests were performed according to test protocols found in the SHRP Interim Guide for Laboratory Material Handling and Testing.⁽²⁾

Laboratory testing includes both the material preparation and the actual testing of samples. Testing is done only by accredited laboratories. Accredited laboratories are expected to provide sufficient and suitable materials testing equipment, facilities, and personnel to meet the requirements of ASTM E329-77, ASTM D3666-83, ASTM D3740-80, and the AASHTO Accreditation Program (AAP), as outlined in the AASHTO Technical Provisions.^{(3, 4)}

Material sampling and testing is performed at least once at the beginning of the section's acceptance into the LTPP program. Additional testing may be performed because of the study requirements or to investigate unexpectedly poor performance. Materials test data are stored in the Materials module in the LTPP database.

LTPP material data processing begins with sample retrieval in the field and continues throughout material characterization until the test results and associated data are placed in the LTPP database at the highest data quality level. Several QA/QC checks are built into the data processing mechanism to ensure that the final data are of the highest quality. Step-by-step operational procedures to ensure QA/QC can be found in several SHRP publications, including LTPP IMS Data Quality Checks and the SHRP Interim Guide for Laboratory Material Handling and Testing.^{(2, 6)} The following basic definitions related to quality management terminology are used in the SHRP-LTPP material characterization program:

The QA/QC program provides for review, assessment, and necessary corrective actions of the following:

The LTPP regional offices perform various QC checks on the data during processing.^{(1, 6, 7)} QC begins during data collection to ensure that material data are collected under comparable conditions, using similar test equipment and testing procedures. QC procedures include review of inputs before and after entry into the LTPP database and checking for errors related to keystroke input, laboratory and field operations and procedures, and test equipment operations.^{(6, 7)}

Other checks, some of which are incorporated in data preprocessing software, review the data by checking for the presence of mandatory data elements (e.g., material description), logic in the data, and range of the specific data values. The QC checks are categorized as levels A through E, as follows:^{(1, 6)}

²For a complete list of critical elements, refer to appropriate FHWA QA/QC manuals.

Each data record in the LTPP database includes a letter showing the last QC check that was performed successfully. A quality value of B does not necessarily indicate all QC after level B was unsuccessful; however, it does indicate a problem with the data record, such as a missing supporting data element (e.g., missing layer number or layer description).

The two important tables in the Materials module that describe the pavement structure and are used to link test results with the pavement layers are TST_L05A and TST_L05B. Other important tables that are necessary to describe and fully understand the test pavement structure are EXPERIMENT_SECTION and COMMENTS. Linking these tables with specific material data tables helps the user to determine consistency in the data and, hence, the overall data quality. Additional information on field sampling, laboratory characterization, and material-related data in the LTPP database can be obtained from the FHWA LTPP Web page, http://www.tfhrc.gov/ltpp.htm.

The material characterization needs of pavement analysts are wide ranging--from standard simple index tests, such as Atterberg limits or gradation of soils, to more rigorous testing of varying complexity, such as coefficient of thermal expansion (CTE), used as input for mechanistic-based analysis. To focus the effort of this study on the most important aspects of the materials data, key data elements were selected to be studied in depth. Selection was based on the following criteria:

Table 1 presents a list of the key material-related data elements selected for evaluation in this study. These data elements describe fundamental pavement characteristics and will be useful in pavement evaluation and research. The list was developed based on the following criteria:

SHRP Test Protocol	Laboratory Test Title	Test Table Designation
P01	Core examination and thickness	TST_AC01
P02	AC bulk specific gravity	TST_AC02
P03	AC maximum specific gravity	TST_AC03
P05	Moisture susceptibility¹	TST_AC05
P14	Gradation of aggregate	TST_AG04
P32	Unconfined compressive strength of treated base/subbase material	TST_TB02
P41	Particle size analysis of granular base/subbase	TST_UG01_UG02_SS01
P43	Determination of Atterberg limits (subgrade)	TST_SS03
P54	Unconfined compressive strength of subgrade soils²	TST_SS10
P61	Determination of compressive strength of in-place concrete³	TST_PC01
P63	Coefficient of thermal expansion for PCC	TST_PC03
P66	Visual examination and length measurement of PCC cores	TST_PC06
P69	PCC flexural strength	TST_PC09

¹Recent research indicates this test may not be very reliable; however, it is currently the LTPP-designated test method for assessing AC susceptibility to moisture damage.
²Tests were conducted on both reconstituted and undisturbed (Shelby tubes) specimens.
³In-place concrete refers to concrete cores specimens extracted from the in-place PCC slab for laboratory testing and not NDT.

Some tables with limited data were evaluated because of their importance (e.g., TST_AC05--moisture susceptibility test). The resilient modulus data for AC and unbound materials were or will be evaluated in separate investigations. A brief description of the data elements selected for evaluation is presented in the next few sections.

Both AC and PCC layer thickness were evaluated as part of this study. These are key inputs required for virtually any analysis related to pavement structural capacity and performance prediction. In addition, AC and PCC thickness are required for computing other pavement material properties, such as backcal-culated layer moduli.

Bulk specific gravity (BSG) and maximum specific gravity (MSG) are important AC mixture properties. They are used in mix design and for QA/QC during construction. Both the bulk and maximum specific gravities are used as the basis for assessing and computing volumetric parameters of AC mixtures, such as air voids, voids in mineral aggregates (VMA), voids filled with asphalt (VFA), and relative compaction of the mixture.

Gradation data from extracted AC cores, unbound base, subbase, and subgrade materials were evaluated as part of this study. AC gradation is a key input for determining the adequacy of the AC design mix and for estimating the volumetric properties of the mixture. For the unbound materials, gradation is a key input for determining numerous material characteristics, including permeability, porosity, effective porosity, coefficient of uniformity, coefficient of gradation/curvature, and soil classification. Gradation can also be used to estimate the stiffness and stability parameters of AC mixtures and base/subbase materials.

A significant proportion of early failures in AC pavements has been linked to durability-related problems with the AC mixtures. Therefore, asphalt-treated materials must be designed at all times to prevent stripping, which is the most significant durability-related distress in AC materials. Stripping can be caused by several factors, including:

Moisture susceptibility tests have been designed to estimate the susceptibility of an AC material to moisture damage, hence, material durability performance. There is, however, no consensus on the usefulness of current moisture susceptibility tests because of conflicting findings reported by various research studies on the reliability of such test results. The test methods currently available, however, offer the best indicators for assessing the long-term durability and performance of AC paving materials, which is key to research.

AC moisture susceptibility testing data in the LTPP database provide the necessary information required for assessing the suitability of aggregate and AC materials for pavement design to prevent early failure.

Compressive strength is an important parameter in characterizing many bound and unbound materials. The primary use of this parameter is in model development, in which performance characteristics are related to strength parameters. Additional uses include developing correlations with strength and deformation parameters, material classification, and others.

Atterberg limits are used in soil classification and to differentiate plastic versus nonplastic fines. These indices are important in assessing permanent deformation tendencies, analysis of subgrade rutting, moisture sensitivity, potential for moisture or freezing-induced volume changes, and more.

Compressive strength is the most widely used measure of PCC quality and frequently serves as the basis for acceptance of the material during construction. The compressive strength can be correlated with the flexural strength, tensile strength, and elastic modulus of the PCC. This test is generally regarded as the easiest of the standard tests to perform on PCC and, therefore, will continue to be widely used in characterization.

The CTE is related to the stresses developed within PCC pavements due to temperature changes. It is very important for mechanistic analysis because it is key for determining thermal-induced stress cycles within PCC pavements. Procedures for determining CTE have recently been developed by the LTPP, and therefore, this study provided an opportunity for characterizing the reasonableness of test results and estimating typical test values.

The flexural strength of PCC is a key parameter in concrete pavement analysis because the flexural test simulates the most common mode of failure in concrete slabs. It is used in M-E analysis to estimate PCC fatigue life, top-down and bottom-up cracking in jointed concrete pavements (JCP), and punchouts in continuously reinforced concrete pavements (CRCP).

Because of its importance, the flexural strength of cast or sawed PCC beams has been related to or correlated with other relatively easy- to-obtain PCC strength parameters, such as compressive strength. The LTPP database is one of the few with both compressive and flexural strength data for PCC pavements that could be used in verifying the accuracy of current models relating PCC flexural and compressive strength or for developing new models, if required.

Various statistical and analytical techniques were adopted and used in this effort. The techniques presented in this chapter were applicable to most of the data elements examined. However, the analysis methods were modified to suit specific situations, where necessary.(8, 9) The procedure for achieving the objectives of this study is presented in figure 2.

The first step after selecting key data elements was to assemble the data from the LTPP database. The following data tables were extracted from the following database tables:

The data used in the study were obtained from the January 2000 release of the LTPP database. The selected material test data tables were merged (using SHRP identification number, construction number, and layer number) with other inventory and test data tables, such as EXPERIMENT_SECTION and TST_LO5B, to obtain information about the pavement structure, including layer descriptions, experiment type, and age. Data acquisition was facilitated by using Microsoft Access, Microsoft Excel.⁽¹⁰⁾

Data elements were examined for completeness at two levels and are defined as follows:

Analysis cells are specific to an experiment and test data element, and are defined based on the following factors:

In general, for the GPS experiments, an analysis cell is the same as a layer of a given material type within a monitoring test section. However, for SPS experiments, the definition of an analysis cell is more complicated because factors such as specimen age at testing, target strength, or target thickness of specimens within a given experiment must be considered in defining cells. Also, using layer numbers for defining analysis cells in SPS experiments may be misleading because the same layer number may be prescribed to different material types along adjacent test sections within the experiment (e.g., dense-graded aggregate base [DGAB] in test section 201 may have a layer number 2 while a DGAB in test section 205 may have a layer number 3).

Finally, the sampling locations for the typical SPS experiment did not necessarily include all 12 individual test sections in the experiment. However, analysis cells were defined such that they represented test sections within the experiment with the given material or layer description (e.g., asphalt content of all asphalt-treated base material or all hot mix asphalt concrete [HMAC]). Figure 3 shows an example of how analysis cells could be defined for GPS pavement test sections. Level 2 data analysis and all other analyses thereafter were done using only level E data.

The objective of level 1 data completeness was to estimate the amount of data still moving through QC checks and data that failed QA/QC. Data still undergoing QA/QC may be available for use in analysis at a later date. Rejected data are those that were collected and entered into the LTPP database but failed QC and are, therefore, held at a quality level less than E, unsuitable for release and use by the public. Level 1 data completeness analysis did not include data that had not yet been entered into the LTPP database.

Level 2 data completeness consisted of the determination (for each test table evaluated) of the number of analysis cells with the minimum number of tests conducted and reported at level E, as required by the applicable data collection guidelines. This was done by comparing the actual number of test results reported per analysis cell with the minimum required. Level 2 data completeness was reported as the percentage of the total number of analysis cells in a test table with the minimum number of test results.

Level 2 data completeness is important because it relates directly to data quality. If an inadequate number of samples/specimens are tested for a given analysis cell within a test section, the resulting representative test results reported for the cell may be biased. Biased representative test results may not be a true reflection of the analysis cell within the pavement test section's material properties.

Data reasonableness was determined using univariate analysis, scatter plots, and bivariate (time-series) plots. The procedures used are described in the following sections.

Data reasonableness was determined by developing scatter plots of the data or performing a univariate analysis to determine the range of the values of the test data. The range of test values was then compared with the range of typical test values to determine whether the LTPP test results were typical. Determining typical values depended on many factors related to both material properties and testing method.⁽¹¹⁾ As an example, the typical flexural strength of a PCC core depends on the mix properties (e.g., cement content, water/cement ratio, and coarse aggregate content), age at testing (e.g., 7-, 14-, 28-day, or long-term testing), and test method used (e.g., specimen dimensions, rate of loading, and test type [center- or third-point loading]).⁽¹¹⁾

Figure 4. Flow chart for assessing data quality.

An example of the information required for evaluating the reasonableness of thickness data is presented in table 2 and figure 5. Both table 2 and figure 5 show the range of thickness values observed for a given database. The observed or calculated range can easily be compared with typical values to determine reasonableness. The information presented can also be used to identify obvious anomalies, such as negative thickness values, thickness values close to zero, or extraordinarily high (e.g., 1,000-mm) thickness values.⁽¹⁾

Analysis cells with erroneous test results are likely to exhibit excessive variability. For LTPP test sections with target test values (e.g., designed thickness = 200 mm), the information assembled by the univariate analysis or scatter plots can be used to determine whether the specified target values were obtained. Test results that are not close to the intended target values do not necessarily imply the presence of anomalies. Such results mean only that the targets were not achieved.

Table 2. Descriptive statistics for evaluating reasonableness of test data.

Cell Type (layer 1, SHRP_ID 001) Number of Specimens Mean Thickness, mm Target Thickness, mm¹ Min Thickness Range, mm Max Thickness Range, mm

GPS-XX

GPS-XX

SPS-XX

SPS-XX

¹If applicable.

Figure 5. Scatter diagram used in assessing reasonableness of data.

The focus of this study was not to identify experiments not achieving the target material properties; however, variability from set targets is an indication of poor construction quality control or poor measurement and the potential for excessive variability in test results.

It was not possible to determine typical test values for all data elements. For such data elements (e.g., gradation), reasonableness was determined only by observing the trends in the data by performing a comprehensive bivariate analysis (e.g., for gradation test results, the percentages passing consecutive sieve sizes are expected to decrease as the sieve size decreases).

Bivariate Analysis

Bivariate plots were developed for time-series test results or for test results with expected trends to determine the reasonableness of observed trends. For example, time-series plots were used in determining data reasonableness for compressive and flexural strength of PCC cores. Past research and analysis has shown that there will most likely be an increase in PCC strength with increasing age. The rate of increase is quite rapid within the first few days after placement and subsequently decreases with age. Therefore, reasonable data are expected to show such a trend. Compressive strength test results showing the opposite trend indicate potential erroneous data. An example of a time-series plot is shown in figure 6.

Data Quality

Data quality was evaluated by assessing the data within analysis cells for sampling and testing bias, compliance with test protocols or standards, and excessive variability. The procedures used in evaluating data quality are discussed in the following sections.

Assessing Data for Sampling or Testing Bias

Bias is a systematic error in testing or sampling that contributes to the difference between sample mean and a true reference value (population mean). Poor sampling methods and procedures, using noncalibrated test equipment, or untrained laboratory personnel are the usual causes for bias.

Figure 6. Time-series plot used in data quality evaluation.

Figure 6. Time-series plot used in data quality evaluation. The graph has Increasing Age on the horizontal axis and portland cement concrete (PCC) Compressive Strength on the vertical axis, with a sample line showing strength increasing with age.

The LTPP material characterization plan is very comprehensive and should eliminate bias in computed mean test values. However, the mean test values for analysis cells with incomplete sampling and testing may be biased, especially if all the limited number of samples tested were collected from either the approach or leave ends of the test pavement. Results from such test sections (where sampling was incomplete) must be evaluated for potential bias to avoid placing unrepresentative test results in the LTPP database for use in research and analysis. Figure 7 shows examples of incomplete sampling that may lead to bias in test results.

Assessment of Compliance with Test Protocols or Procedures

Another potential source of error, variability, and bias in test results is the lack of compliance with test procedures. Compliance with test protocols involves any or all of the following:

Sample extraction and preparation.
Sample storage and handling.
Test equipment.
Procedures for computing test results and units of measurement.
Precision of measurements and rounding up of test values.

Figure 7. Examples of sampling bias.

Figure 7. Examples of sampling bias. The example shows Traffic Direction going from left to right and the shoulder closest to the observer. The section has the Sampling Area first, then the

Assessment of Within-Cell Variability

Statistics such as standard deviation and coefficient of variation (COV) were used to characterize within-cell variability. Variability can be computed only for analysis cells with multiple data points (multiple test results reported for a given analysis cell). The following are definitions of the statistics used in assessing variability:⁽¹²⁾

Variance--a measure of the scatter or spread in a given data set. It is defined as the sum of the squared deviations of each observation from the sample average, divided by the sample size minus 1 (see equation 1).⁽¹²⁾ Because variance is expressed as the square of the units of the data element being analyzed, it is not always readily understood because it is not in the native units of the data element being evaluated.

Equation 1: S squared = (the summation from i=1 to n of (X subscript 1 minus X subscript m) squared) over (n minus 1)

where:

X_i = Test result from the i^th specimen
X_m = Sample mean
S = Sample standard deviation
n = Number of specimens

Standard Deviation--a measure of the scatter or spread in a given data set. It is defined as the square root of the sum of the squared deviations of each observation from the sample average divided by the sample size minus one (see equation 2).⁽¹²⁾ Standard deviation is expressed in the units of the data element being analyzed and is therefore more easily understood when evaluating variability.

Equation 2: S = the square root of ((the summation from i=1 to n of (X subscript 1 minus X subscript m) squared) over (n minus 1))

Coefficient of Variation--the ratio of standard deviation and sample mean. It is defined as follows:⁽¹²⁾

Equation 3: CV = S over X subscript m

The sample mean used in calculating the COV is the average of all individual test results for a given cell. It is a measure of the central tendency of the test results and is defined as:⁽¹²⁾

Equation 4: X subscript m = (the summation from i=1 to n of X) over n

These statistics (calculated for each analysis cell) were then compared with typical allowable variability to assess data quality.

Establishing Typical Variability

The fundamental statistic underlying all indices of typical variability is standard deviation. Typical variability measured as standard deviation is a summation of the following:⁽¹³⁾

Material variability.
Testing variability (operator and equipment).
Sampling variability.

They are summed up as shown in figure 8. Typical variability can be computed as follows:

Figure 8. Summation of testing, sampling, and material variability to yield typical variability.⁽¹³⁾

Figure 8. Summation of testing, sampling, and material variability to yield typical variability (13).

1. Compute variability due to sampling and testing:

Equation 5: (sigma subscript ST) squared = (sigma subscript S) squared plus (sigma subscript T) squared

where:

sigma_ST = Variability due to sampling and testing
sigma_S = Sampling variability
sigma_T = Testing variability

2. Compute typical variability

where:

sigma_TYP = Typical variability
sigma_ST = Variability due to sampling and testing
sigma_M = Material variability

The significance of each of these components is discussed in the next few sections.

Material Variability--Material variability is the true random variability of any paving material. It is a function of the characteristics of the material itself and, therefore, varies in magnitude from material to material. Several studies have shown that material variability is one of the smallest sources of variability in test results in projects with adequate QA/QC.⁽¹³⁾

Sampling Variability--Sampling variability is a function of sampling technique, material, testing, and construction variability. It is detected when a sample taken from one location of a pavement will not indicate the same test result as one taken from another location of the same pavement.⁽¹³⁾ Sampling variability can be assessed at two levels, namely, within-location and location-to-location.

Within-location variability is the magnitude of the difference in the measurements between two or more samples taken from the same location within the pavement. Within-location variability is a function of the sampling technique, material, and testing variability. Classic examples of within-location variability are variations in core thicknesses and core strengths of a concrete pavement for adjacent cores in the same location.⁽¹³⁾

Location-to-location variability is usually the largest source of variability in the paving process and, hence, paving materials. It represents the difference in test results from one location to other locations of the same material from the same pavement.⁽¹³⁾ It includes all the causes of within-location sampling variability and construction variability by the paving process. Location-to-location sampling variability is greatest when the paving process is termed out-of-control. This type of variability is best exposed through multiple sampling along the pavement.

Testing Variability--Testing variability is the lack of repeatability of test results between test samples. It includes the effects of reducing sample increments to test portion size. Operators, equipment condition and calibration, and test procedure are a few of the important factors that can cause high testing variability.⁽¹³⁾ Testing variability is often expressed as a precision statement.^{(12, 13)}

Precision statements for test procedures provide guidance on the magnitude of variability that can be expected between test results when the same test method is used in one or more laboratories. ASTM E177, Standard Practice for Use of the Terms Precision and Bias in ASTM Test Methods, discusses the concepts used in developing precision statements for various types of tests in great detail.⁽¹²⁾ The precision of a measurement process is a generic concept related to the closeness of agreement between test results obtained under prescribed like conditions from the measurement process being evaluated. Two kinds of precision statements are commonly used in assessing testing variability: within-laboratory precision (sometimes called single-operator precision) and between-laboratory precision (called multilaboratory precision). ASTM C670, Preparing Precision and Bias Statements for Test Methods for Construction Materials, defines the two types of precision statements.⁽¹²⁾

For this study, typical variability incorporated all possible sources of variability (i.e., material, sampling, testing, and construction variability). Typical variability was established as close to the expected conditions of sampling and testing as possible. For this reason, the conditions under which variability is observed and reported in published literature was considered before being adopted for use in establishing typical variability.^{(13, 14)}

Relationship between Variability, Accuracy, and Bias

The terms variability, accuracy, and bias are often confused and may be used misleadingly. Accuracy is a concept of exactness related to the closeness of agreement between the average of one or more test results and an accepted reference value. Accuracy may be thought of as an absence of bias--the consistent or systematic difference between a set of test results from a process and the true value, or reference value, of the property being measured. The definitions of precision, accuracy, and bias are best explained using a series of bulls-eye targets, shown in figure 9. Excessive variability does not always result in inaccurate average values, when compared with the true reference average value. It could, however, introduce bias and error. It is, therefore, a cause of concern and should be limited as much as possible. Bias always leads to erroneous results and should be avoided.

Recommendations for Remedial Action to Correct Identified Anomalies

The preceding sections of this chapter discussed the methods that were applied to identify potential anomalies in the selected LTPP material test data. The next step was to perform remedial action where possible to correct the identified anomalies. This section presents a general overview of some of the common anomalies with suggested remedial action. It must be noted that some anomalies are unique to the particular data element, test conditions, and LTPP test section to be evaluated. Such anomalies and remedial actions to correct them will be explained and discussed throughout this report.

Identification of Anomalous Data

Table 3 lists common anomalies, their potential effects on data quality, and potential remedial actions. The anomalies listed are the most commonly encountered in data analysis for completeness and quality.

Figure 9. Relationship between precision, accuracy, and bias.⁽¹³⁾

Table 3. Potential anomalies in material test data and recommended remedial action.

Identified Anomaly Effect on Data Quality Recommended Remedial Action

Insufficient data at level E due to test results still undergoing QA/QC at levels A to D Data are inadequate for analysis (data might not represent test section) Upgrade representative data tables when QA/QC process is complete

Insufficient data at level E due to insufficient testing and/or test results failing QA/QC checks between levels A to D Data are inadequate for analysis (data might not represent test section) Adopt representative values from similar sections

Insufficient data at level E due to insufficient testing and/or test results failing QA/QC checks between levels A to D Data are inadequate for analysis (data might not represent test section) Backcast or forecast from time-series data

Insufficient data at level E due to insufficient testing and/or test results failing QA/QC checks between levels A to D Data are inadequate for analysis (data might not represent test section) Estimate data from analytical models or techniques

Inadequate sampling (e.g., single test values) Possible bias due to unrepresentative test values Check sampling locations to verify that the data adequately represent test section

Inadequate sampling (e.g., single test values) Possible bias due to unrepresentative test values Resampling and testing to obtain more representative test results

Noncompliance with testing protocol Excessive variability in multiple test results (assuming only a few of the test values are noncompliant) Identify individual noncompliant test values as potential outliers and study their effect on variability

Noncompliance with testing protocol Potential for systematic bias if all tests on multiple specimens are noncompliant. Perform forensic testing

Unreasonable multiple test values Excessive variability in multiple test results (assuming only a few of the test values are unreasonable) Identify individual unreasonable test values as potential outliers and study their effect on variability. Remove outliers from developing representative data tables if they are the cause of the excessive variability

Unreasonable single test values Unreliable data Perform forensic testing

Unexplained excessive variability Unreliable data Identify potential outliers and errors using statistical techniques (ASTM E178)

Missing experiment type and layer information -- Consult materials testing and construction data sheets or other data tables with the database to determine the required information

Missing specimen testing age or anomalous computed ages -- Consult materials testing and construction data sheets or other data tables with the database to determine the required information

Identified Anomaly	Effect on Data Quality	Recommended Remedial Action
Insufficient data at level E due to test results still undergoing QA/QC at levels A to D	Data are inadequate for analysis (data might not represent test section)	Upgrade representative data tables when QA/QC process is complete
Insufficient data at level E due to insufficient testing and/or test results failing QA/QC checks between levels A to D	Data are inadequate for analysis (data might not represent test section)	Adopt representative values from similar sections
Insufficient data at level E due to insufficient testing and/or test results failing QA/QC checks between levels A to D	Data are inadequate for analysis (data might not represent test section)	Backcast or forecast from time-series data
Insufficient data at level E due to insufficient testing and/or test results failing QA/QC checks between levels A to D	Data are inadequate for analysis (data might not represent test section)	Estimate data from analytical models or techniques
Inadequate sampling (e.g., single test values)	Possible bias due to unrepresentative test values	Check sampling locations to verify that the data adequately represent test section
Inadequate sampling (e.g., single test values)	Possible bias due to unrepresentative test values	Resampling and testing to obtain more representative test results
Noncompliance with testing protocol	Excessive variability in multiple test results (assuming only a few of the test values are noncompliant)	Identify individual noncompliant test values as potential outliers and study their effect on variability
Noncompliance with testing protocol	Potential for systematic bias if all tests on multiple specimens are noncompliant.	Perform forensic testing
Unreasonable multiple test values	Excessive variability in multiple test results (assuming only a few of the test values are unreasonable)	Identify individual unreasonable test values as potential outliers and study their effect on variability. Remove outliers from developing representative data tables if they are the cause of the excessive variability
Unreasonable single test values	Unreliable data	Perform forensic testing
Unexplained excessive variability	Unreliable data	Identify potential outliers and errors using statistical techniques (ASTM E178)
Missing experiment type and layer information	--	Consult materials testing and construction data sheets or other data tables with the database to determine the required information
Missing specimen testing age or anomalous computed ages	--	Consult materials testing and construction data sheets or other data tables with the database to determine the required information

Although the potential impact of individual anomalies on data quality is clear, the effect of the interactions of various anomalies is not. Other possible remedial actions not presented in table 3 are discussed below.

Outlier Analysis

Outliers can cause excessive variability in multiple test results. They can be identified by checking for compliance with the testing and sampling procedures and comparing the test results with typical values, or through statistical analysis. The former two methods are straightforward and have already been discussed.

When it is clear that the source of excessive variability cannot be attributed to any known cause, statistical analysis can be performed to determine whether the data point is, indeed, a true outlier. There are a number of statistical tests and criteria for identifying outliers within a group of test results. For this study, the recommended procedure in ASTM E178, Standard Practice for Dealing with Outlying Observations, was used for outlier identification.⁽¹²⁾ The procedure is summarized as follows:

Calculate the numerical value of a sample test statistic (T-statistic), using all test values (include the doubtful test result or observation in the calculation).
Compare the calculated test statistic with a critical value of the statistic based on the theory of random sampling to determine whether the doubtful observation is to be retained or rejected.

The critical value is the value that the calculated sample test statistic would exceed by chance with some specified (small) probability. This is based on the assumption that all the observations did, indeed, constitute a random sample from a common system of causes, a single parent population, distribution, or universe. The specified small probability is called the significance level, the choice of which depends on the complexities and circumstances of the problem under investigation and the risk that one is willing to take in rejecting a good observation.

Further, almost all criteria for determining outliers are based on the assumption that the population or distribution of test results is normal or approximately normal. Outlier analysis based on data not normally or approximately normally distributed could result in erroneous conclusions.

Resampling and Testing

When there is inadequate sampling information at level E and most data at lower levels failed LTPP QA/QC checks, the only way to obtain information that is representative of the test section is to resample and test again. Although highly unusual, this might be necessary in some cases.

Forensic Testing

When there is a reason to believe that the testing was not performed in compliance with test protocols, forensic testing might be a viable option to obtain more representative test values.

4. AC CORE EXAMINATION AND THICKNESS

Introduction

Variation in layer thickness has a significant influence on the structural characteristics and performance of in-service pavements. Variable asphalt pavement layer thicknesses affect pavement characteristics such as back-calculated layer moduli, key input for characterizing the structures adequacy of an existing pavement and for the design of overlays. It is, therefore, necessary to minimize thickness variability for all pavement layers, especially AC layers.

Collecting AC layer thickness data is an important aspect of the LTPP material characterization program. Thickness data are obtained from AC cores extracted from selected locations at the approach and leave ends of the pavement test section. The cores are also examined for possible defects and suitability for testing. AC core examination and thickness measurements were done based on SHRP protocol P01--Visual Examination and Thickness of Asphaltic Concrete Cores--and the test standard AASHTO T148--Measuring Length of Drilled Concrete Cores (ASTM C174).^{(2, 3, 4)}

The test protocol and standard provide guidance on material sampling, preparation and testing of specimens, computation, and presentation of test results. The test results are stored in the LTPP database (table TST_AC01_LAYER) after undergoing several levels of quality checks. Data classified at level E have undergone and successfully passed all the QA/QC checks required by the LTPP. Data classified at levels A to D may still be undergoing QA/QC or may have failed QA/QC. Table TST_AC01_LAYER contains the following information:

SHRP_ID.
State code.
Field layer number.
Field set.
Test number.
Layer number.
Location number.
Construction number.
Field layer comment.
Layer description.
Layer thickness.
Record status.

Material Sampling for AC Core Thickness

Material sampling was performed according to guidelines provided in several LTPP documents and reports, including the SHRP-LTPP Interim Guide for Laboratory Material Handling and Testing and the SPS Guidelines for Nominations and Evaluation of Candidate Projects. (See references 2, 15 through 20.) For GPS experiments, test samples were collected at specific locations outside the monitoring sections of the LTPP test sections. For SPS projects, cores were extracted from designated locations adjacent to the pavement test sections. Core thickness examination and thickness measurements were performed on all cores retrieved. Basically, two types of cores were extracted: (See references 2, 15, 1through 20.)

Type A--152-mm diameter cores obtained from the approach and leave ends of a monitoring section.
Type C--102-mm diameter cores obtained from the approach and leave ends of a monitoring section.

Sampling and testing requirements for AC core examination and thickness testing are presented in tables 4 and 5. The tables show the minimum number of core specimens required for testing for the various LTPP experiments, along with the sampling locations. The sampling requirements were used to define analysis cells for data completeness and quality evaluation, as follows:

For GPS experiments, an analysis cell was defined as any asphalt-treated layer (including the surface layer) within a given test section.
For SPS experiments, an analysis cell was defined as a given asphalt-treated material type within the SPS experiment. Asphalt-treated layers with the same materials descriptions (e.g., permeable asphalt-treated base material [PATB]) located in different test sections (e.g., 102 or 103) were considered as belonging to the same analysis cell.

The data fields used for defining analysis cells for GPS and SPS test sections are presented in table 6.

AC Core Data Completeness

The AC core data completeness evaluation was conducted at two levels. The level 1 data completeness evaluation involved the determination of the amount of the total data available in table TST_AC01_LAYER, the percentage at level E, and the number of analysis cells represented by the level E data. Level 2 data completeness consisted of determining the percentage of analysis cells with the minimum required number of test results reported at level E. The January 2000 release of table TST_AC01_LAYER was used for the analyses.

Table 4. Sampling requirements for visual examination and thickness of AC cores.

Experiment Type Layer Type LTPP Designation SHRP Protocol Minimum Number of Tests per Layer Sampling Location

GPS 1, 2, 6, and 7 AC AC01 P01 16 All 100-mm and 150-mm-diameter cores

SPS-1 Asphalt treated base AC01 P01 34 102-mm OD coresC1-C10, C21-C34,C47-C56

SPS-1 AC surface and binder AC01 P01 60 102-mm OD coresC1-C60

SPS-3 Asphalt treated base AC01 P01 34 102-mm OD coresC1-C10, C21-C34,C47-C56

SPS-3 AC surface and binder AC01 P01 60 102-mm OD coresC1-C60

SPS-5 Preconstruction AC AC01 P01 26 All Type-C cores

SPS-5 Postconstruction AC AC01 P01 40 All cores

SPS-6 AC AC01 P01 20 All cores

SPS-8 AC AC01 P01 16 All cores

SPS-9 Preconstruction AC AC01 P01 6 A01A01,A02A01,A01A02A02A02,A01A03,A02A03

SPS-9 Postconstruction AC AC01 P01 8 --

Experiment Type	Layer Type	LTPP Designation	SHRP Protocol	Minimum Number of Tests per Layer	Sampling Location
GPS 1, 2, 6, and 7	AC	AC01	P01	16	All 100-mm and 150-mm-diameter cores
SPS-1	Asphalt treated base	AC01	P01	34	102-mm OD coresC1-C10, C21-C34,C47-C56
SPS-1	AC surface and binder	AC01	P01	60	102-mm OD coresC1-C60
SPS-3	Asphalt treated base	AC01	P01	34	102-mm OD coresC1-C10, C21-C34,C47-C56
SPS-3	AC surface and binder	AC01	P01	60	102-mm OD coresC1-C60
SPS-5 Preconstruction	AC	AC01	P01	26	All Type-C cores
SPS-5 Postconstruction	AC	AC01	P01	40	All cores
SPS-6	AC	AC01	P01	20	All cores
SPS-8	AC	AC01	P01	16	All cores
SPS-9 Preconstruction	AC	AC01	P01	6	A01A01,A02A01,A01A02A02A02,A01A03,A02A03
SPS-9 Postconstruction	AC	AC01	P01	8	--

OD = outside diameter.

Level 1--Data Completeness

The first step in assessing data completeness was the extraction and assembly of the thickness and visual examination test data from the LTPP database. The layer and material description information in table TST_AC01_LAYER was cross-referenced with similar information in other LTPP material-related test tables, such as TST_ LO5B and EXPERIMENT_SECTION, by combining these tables with TST_AC01_LAYER. Cross-referencing the data made it possible to check for anomalies in material description, layer type, and layer number information in table TST_AC01_LAYER. Test results or records with anomalies in material and layer information were flagged for further evaluation. The results of the level 1 data completeness analysis are presented in table 7.

Table 5. Details of sampling requirements for visual examination and thickness of AC cores for SPS experiments.

Experiment Type Test Section Material Description Min. No. of Cores Required(Surface and binder/ATB) Sampling Location

SPS-1 0101 (0113) AC 6/0 C41-C46

SPS-1 0102 (0114) AC 4/0 C57-C60

SPS-1 0103 (0115) AC 4/4 C47-C50

SPS-1 0104 (0116) AC 4/4 C1-C4

SPS-1 0105 (0117) AC 6/6 C51-C56

SPS-1 0106 (0118) AC 6/6 C5-C9

SPS-1 0107 (0119) AC 6/0 C35-C40

SPS-1 0108 (0120) AC 6/0 C15-C20

SPS-1 0109 (0121) AC 4/0 C11-C14

SPS-1 0110 (0122) AC 4/4 C21-C24

SPS-1 0111 (0123) AC 4/4 C31-C34

SPS-1 0112 (0124) AC 6/4 C25-C30

SPS-5 (Preconstruction) 0501 AC 2 C1,C2

SPS-5 (Preconstruction) 0502 AC 4 C3-C6

SPS-5 (Preconstruction) 0503 AC 2 C7-C8

SPS-5 (Preconstruction) 0504 AC 2 C9-C10

SPS-5 (Preconstruction) 0505 AC 2 C11-C12

SPS-5 (Preconstruction) 0506 AC 5 C13-C17

SPS-5 (Preconstruction) 0507 AC 2 C18-C19

SPS-5 (Preconstruction) 0508 AC 5 C20-C24

SPS-5 (Preconstruction) 0509 AC 2 C25-C26

SPS-5 (Postconstruction) 0501 AC 0 --

SPS-5 (Postconstruction) 0502 AC 4 C27-C30

SPS-5 (Postconstruction) 0503 AC 6 C31-C35

SPS-5 (Postconstruction) 0504 AC 6 C37-C42

SPS-5 (Postconstruction) 0505 AC 4 C43-C46

SPS-5 (Postconstruction) 0506 AC 4 C47-C50

SPS-5 (Postconstruction) 0507 AC 6 C51-C56

SPS-5 (Postconstruction) 0508 AC 6 C57-C62

SPS-5 (Postconstruction) 0509 AC 4 C63-C66

SPS-6 (Preconstruction) 0601 AC 3 C1-C3

SPS-6 (Preconstruction) 0602 AC 3 C3-C6

SPS-6 (Preconstruction) 0603 AC 2 C11-C12

SPS-6 (Preconstruction) 0604 AC 2 C13-C14

SPS-6 (Preconstruction) 0605 AC 4 C7-C10

SPS-6 (Preconstruction) 0606 AC 2 C15-C16

SPS-6 (Preconstruction) 0607 AC 2 C17-C18

SPS-6 (Preconstruction) 0608 AC 2 C19-C20

SPS-6 (Postconstruction) 0603 AC 4 C21-C24

SPS-6 (Postconstruction) 0604 AC 4 C25-C28

SPS-6 (Postconstruction) 0606 AC 4 C29-C32

SPS-6 (Postconstruction) 0607 AC 4 C33-C36

SPS-6 (Postconstruction) 0608 AC 4 C37-C40

SPS-8 0801, 0803, 0805 AC 8 C1-C8

SPS-8 0802, 0804, 0806 AC 8 C9-C16

SPS-9 0901, 0902, 0903 AC 8 --

Experiment Type	Test Section	Material Description	Min. No. of Cores Required(Surface and binder/ATB)	Sampling Location
SPS-1	0101 (0113)	AC	6/0	C41-C46
SPS-1	0102 (0114)	AC	4/0	C57-C60
SPS-1	0103 (0115)	AC	4/4	C47-C50
SPS-1	0104 (0116)	AC	4/4	C1-C4
SPS-1	0105 (0117)	AC	6/6	C51-C56
SPS-1	0106 (0118)	AC	6/6	C5-C9
SPS-1	0107 (0119)	AC	6/0	C35-C40
SPS-1	0108 (0120)	AC	6/0	C15-C20
SPS-1	0109 (0121)	AC	4/0	C11-C14
SPS-1	0110 (0122)	AC	4/4	C21-C24
SPS-1	0111 (0123)	AC	4/4	C31-C34
SPS-1	0112 (0124)	AC	6/4	C25-C30
SPS-5 (Preconstruction)	0501	AC	2	C1,C2
SPS-5 (Preconstruction)	0502	AC	4	C3-C6
SPS-5 (Preconstruction)	0503	AC	2	C7-C8
SPS-5 (Preconstruction)	0504	AC	2	C9-C10
SPS-5 (Preconstruction)	0505	AC	2	C11-C12
SPS-5 (Preconstruction)	0506	AC	5	C13-C17
SPS-5 (Preconstruction)	0507	AC	2	C18-C19
SPS-5 (Preconstruction)	0508	AC	5	C20-C24
SPS-5 (Preconstruction)	0509	AC	2	C25-C26
SPS-5 (Postconstruction)	0501	AC	0	--
SPS-5 (Postconstruction)	0502	AC	4	C27-C30
SPS-5 (Postconstruction)	0503	AC	6	C31-C35
SPS-5 (Postconstruction)	0504	AC	6	C37-C42
SPS-5 (Postconstruction)	0505	AC	4	C43-C46
SPS-5 (Postconstruction)	0506	AC	4	C47-C50
SPS-5 (Postconstruction)	0507	AC	6	C51-C56
SPS-5 (Postconstruction)	0508	AC	6	C57-C62
SPS-5 (Postconstruction)	0509	AC	4	C63-C66
SPS-6 (Preconstruction)	0601	AC	3	C1-C3
SPS-6 (Preconstruction)	0602	AC	3	C3-C6
SPS-6 (Preconstruction)	0603	AC	2	C11-C12
SPS-6 (Preconstruction)	0604	AC	2	C13-C14
SPS-6 (Preconstruction)	0605	AC	4	C7-C10
SPS-6 (Preconstruction)	0606	AC	2	C15-C16
SPS-6 (Preconstruction)	0607	AC	2	C17-C18
SPS-6 (Preconstruction)	0608	AC	2	C19-C20
SPS-6 (Postconstruction)	0603	AC	4	C21-C24
SPS-6 (Postconstruction)	0604	AC	4	C25-C28
SPS-6 (Postconstruction)	0606	AC	4	C29-C32
SPS-6 (Postconstruction)	0607	AC	4	C33-C36
SPS-6 (Postconstruction)	0608	AC	4	C37-C40
SPS-8	0801, 0803, 0805	AC	8	C1-C8
SPS-8	0802, 0804, 0806	AC	8	C9-C16
SPS-9	0901, 0902, 0903	AC	8	--

ATB = asphalt-treated base.

Table 6. Data fields used for defining analysis cells for AC thickness.

Data Fields GPS SPS

SHRP_ID X X

State Cored X X

Layer number X X

Construction number X X

Data Fields	GPS	SPS
SHRP_ID	X	X
State Cored	X	X
Layer number	X	X
Construction number	X	X

Table 7. Level 1 data completeness for table TST_AC01_LAYER.

Experiment Type Expt. Number Total Number of Records at All Levels Total Number of Records at Level E Percentage of Records at Level E Number of Analysis Cells Represented at Level E

GPS 1 8069 8067 99.6 488

GPS 2 5655 5655 100 349

GPS 3 159 159 100 13

GPS 4 30 30 100 2

GPS 5 275 275 100 22

GPS 6A 3496 3496 100 218

GPS 6B 3359 3359 100 189

GPS 6C 505 500 99 35

GPS 6D 128 128 100 9

GPS 6S 1584 1561 99 104

GPS 7A 1185 1183 99.8 72

GPS 7B 529 529 100 29

GPS 7C 36 36 100 2

GPS 7S 189 189 100 8

GPS 9 322 312 96.9 27

SPS 1 1977 1965 99.4 423

SPS 3 1928 1928 100 958

SPS 5 2577 2550 99 568

SPS 6 248 248 100 64

SPS 8 237 237 100 31

SPS 9 625 559 89.4 52

Experiment Type	Expt. Number	Total Number of Records at All Levels	Total Number of Records at Level E	Percentage of Records at Level E	Number of Analysis Cells Represented at Level E
GPS	1	8069	8067	99.6	488
GPS	2	5655	5655	100	349
GPS	3	159	159	100	13
GPS	4	30	30	100	2
GPS	5	275	275	100	22
GPS	6A	3496	3496	100	218
GPS	6B	3359	3359	100	189
GPS	6C	505	500	99	35
GPS	6D	128	128	100	9
GPS	6S	1584	1561	99	104
GPS	7A	1185	1183	99.8	72
GPS	7B	529	529	100	29
GPS	7C	36	36	100	2
GPS	7S	189	189	100	8
GPS	9	322	312	96.9	27
SPS	1	1977	1965	99.4	423
SPS	3	1928	1928	100	958
SPS	5	2577	2550	99	568
SPS	6	248	248	100	64
SPS	8	237	237	100	31
SPS	9	625	559	89.4	52

Note: There were a total of 1,680 records from SPS supplemental sections not listed in this table.

A total of 34,793 records were available in table TST_AC01_LAYER. Of these, 1,680 were from SPS supplemental sections. Approximately 99 percent of the AC layer thickness data were at level E. The records pertaining to SPS supplemental sections were kept out of the further analysis, because they fall out of the scope of this study.

Level 2--Data Completeness

Analysis cells consisting of level E data were further evaluated to determine whether the minimum required number of tests was performed and reported at level E. This was done by checking the number of test results or records available in each analysis cell and comparing it with the sampling and testing requirements presented in SHRP P01. Analysis cells with at least the minimum number of test records required were categorized as complete, whereas analysis cells will less than the minimum required test results at level E were classified as incomplete. The results of the level 2 data completeness evaluation are presented in table 8.

Table 8. Summary of level 2 data completeness for TST_AC01_LAYER.

Experiment Type Number of Analysis Cells with Data Min. Number of Test Results Required Analysis Cells with Minimum Number of Test Results Percentage Analysis Cells with Minimum Test Results

GPS-1 459 16 392 85.4

GPS-2 340 16 291 85.6

GPS-6A 212 16 186 87.7

GPS-6B 182 16 141 77.5

GPS-6C 30 16 16 53.3

GPS-6D 8 16 3 37.5

GPS-6S 79 16 38 48.1

GPS-7A 65 16 48 73.9

GPS-7B 27 16 18 66.7

GPS-7C 2 16 2 100

GPS-7S 8 16 8 100

SPS-1 228 5/3¹ 135 59.2

SPS-3 914 3 138 15.1

SPS-5 471 3/4² 311 66.0

SPS-6 39 2 36 2.3

SPS-8 21 4 21 100

Experiment Type	Number of Analysis Cells with Data	Min. Number of Test Results Required	Analysis Cells with Minimum Number of Test Results	Percentage Analysis Cells with Minimum Test Results
GPS-1	459	16	392	85.4
GPS-2	340	16	291	85.6
GPS-6A	212	16	186	87.7
GPS-6B	182	16	141	77.5
GPS-6C	30	16	16	53.3
GPS-6D	8	16	3	37.5
GPS-6S	79	16	38	48.1
GPS-7A	65	16	48	73.9
GPS-7B	27	16	18	66.7
GPS-7C	2	16	2	100
GPS-7S	8	16	8	100
SPS-1	228	5/3¹	135	59.2
SPS-3	914	3	138	15.1
SPS-5	471	3/4²	311	66.0
SPS-6	39	2	36	2.3
SPS-8	21	4	21	100

¹For each section in the SPS-1 project, five tests are required for the AC surface and binder layers, and three tests are required for the asphalt treated base.
²For each section in the SPS-5 project, three tests are required on the original AC surface preconstruction, and four tests are required on the overlay postconstruction.

Of the 3,139 analysis cells with test data at level E in table TST_AC01_LAYER, 57 percent (1,784) had the minimum number of test results reported and, therefore, were classified as complete. The remaining 43 percent were incomplete, possibly because of data still undergoing QA/QC, missing test data, inadequate sampling, or untestable AC core specimens. The effect of inadequate data on the data analysis (computing representative test results and basic statistics) will be discussed in later sections of this chapter.

AC Core Visual Examination and Thickness Data Quality

Data quality was evaluated for all the data in table TST_AC01_LAYER. The first step was to evaluate the data for reasonableness by comparing core measurement data with typical AC and asphalt-treated material layer thickness. There is a wide range of "typical" layer thickness; however, negative, extremely small (< 2.5 mm), or extremely high (> 600 mm) thickness values can be classified as unreasonable. Such data will require further evaluation.

The next step was to determine variability for the test data within each analysis cell. The within-cell variability was compared with typical variability reported in published literature and classified as acceptable or questionable, based on whether computed variability was greater or less than typical.

Data Reasonableness

The SHRP-LTPP Interim Guide for Material Handling and Testing provides typical ranges expected for AC layer thickness, based on the layer and material type.⁽²⁾ The typical ranges of thickness values are presented in table 9 for GPS experiments. Table 10 presents a detailed breakdown of the core thickness data and descriptive statistics in TST_AC01_LAYER. For GPS experiments, approximately 99 percent of the cores from overlays had thickness values within the recommended range. The remaining 1 percent was evaluated for potential anomalies.

Similarly, 94 percent of the core specimens from the seal coat layers had thickness values within the recommended range. SPS-1 surface layers have two target thicknesses, 102 mm and 178 mm. The average core thicknesses and standard deviation for test data in the two thickness groups were 107 and 18 mm and 168 and 32 mm, respectively. SPS-5 experiments also have two different target thicknesses, 51- and 127-mm overlays. The average measured thicknesses of the overlays were 74.3 and 150.1 mm for the 51- and 127-mm sections, respectively.

For SPS-6 experiments, the target thicknesses of overlays was 102 and 204 mm. Average thickness values reported for these sections were 104 and 211 mm, respectively, which were very close to the target thickness values. SPS-8 experiments have surface layer target thicknesses of 102 and 178 mm, and the average measured thicknesses for these sections were 104 and 170 mm, which are very close to the target thickness values. Figures 10 through 12 show the distribution of the core specimen thicknesses for selected layer types (overlay, surface, and base). The information presented confirms the trends presented in table 10.

Table 9. Range of thickness for various GPS AC layers.⁽²⁾

LTPP Layer Description Code Number Material/Layer Description Typical Thickness Range, mm No. of Test Records With Thickness within Range No. of Analysis Cells With Thickness within Range Number of Test Records out of range

1 Overlay 12.5 - 150 3848 228 26

2 Seal coat 2.5 - 37.5 1836 115 35

3 Original surface 12.5 - 325 7830 474 72

4 AC layer below surface 12.5 - 250 5712 368 99

5 Base 25 - 600 1253 86 12

6 Subbase 75 - 1200 30 2 0

8 Interlayer 2.5 - 150 574 40 0

9 Friction course 2.5 - 62.5 1838 112 0

10 Surface treatment 2.5 - 37.5 77 5 0

LTPP Layer Description Code Number	Material/Layer Description	Typical Thickness Range, mm	No. of Test Records With Thickness within Range	No. of Analysis Cells With Thickness within Range	Number of Test Records out of range
1	Overlay	12.5 - 150	3848	228	26
2	Seal coat	2.5 - 37.5	1836	115	35
3	Original surface	12.5 - 325	7830	474	72
4	AC layer below surface	12.5 - 250	5712	368	99
5	Base	25 - 600	1253	86	12
6	Subbase	75 - 1200	30	2	0
8	Interlayer	2.5 - 150	574	40	0
9	Friction course	2.5 - 62.5	1838	112	0
10	Surface treatment	2.5 - 37.5	77	5	0

Table 10. Summaries of descriptive statistics for core thickness data in table TST_AC01_LAYER.

Expt. Type Layer Description Target Thickness, mm Number of Core Specimens Mean Thickness, mm Standard Deviation, mm COV, percent Min. Thickness Range, mm Max. Thickness Range, mm

GPS Overlay 12.5 - 150 3874 54.7 27 49.4 2.5 191

GPS Seal coat 2.5 - 37.5 1871 9.3 10 108.0 2.5 97

GPS Original surface 12.5 - 325 7962 68 51 74.6 5.1 401

GPS AC layer below surface 12.5 - 250 5838 88 61 68.9 25 406

GPS Base 25 - 600 1263 142 66 46.4 51 363

GPS Subbase 75 - 1200 30 101 10 10.0 86 127

GPS Interlayer 2.5 - 150 574 25 28 113.4 2.5 109

GPS Friction course 2.5 - 62.5 1838 17 12 66.6 2.5 69

GPS Surface treatment 2.5 - 37.5 77 3.4 2.2 63.6 2.5 10

SPS-1 Surface 102 50 106.5 18.2 17.1 53 147

SPS-1 Surface 178 45 168.2 32.3 16.5 41 201

SPS-5 Overlay 51 45 74.3 23.7 31.9 42 119

SPS-5 Overlay 127 48 150.1 25.6 17.1 114 200

SPS-6 Overlay 102 16 103.7 9.0 8.7 86 120

SPS-6 Overlay 203 4 210.0 7.4 3.5 199 215

SPS-8 Surface 102 7 105.3 10.5 10.0 91 119

SPS-8 Surface 178 7 171.2 15.7 9.2 142 187

Expt. Type	Layer Description	Target Thickness, mm	Number of Core Specimens	Mean Thickness, mm	Standard Deviation, mm	COV, percent	Min. Thickness Range, mm	Max. Thickness Range, mm
GPS	Overlay	12.5 - 150	3874	54.7	27	49.4	2.5	191
GPS	Seal coat	2.5 - 37.5	1871	9.3	10	108.0	2.5	97
GPS	Original surface	12.5 - 325	7962	68	51	74.6	5.1	401
GPS	AC layer below surface	12.5 - 250	5838	88	61	68.9	25	406
GPS	Base	25 - 600	1263	142	66	46.4	51	363
GPS	Subbase	75 - 1200	30	101	10	10.0	86	127
GPS	Interlayer	2.5 - 150	574	25	28	113.4	2.5	109
GPS	Friction course	2.5 - 62.5	1838	17	12	66.6	2.5	69
GPS	Surface treatment	2.5 - 37.5	77	3.4	2.2	63.6	2.5	10
SPS-1	Surface	102	50	106.5	18.2	17.1	53	147
SPS-1	Surface	178	45	168.2	32.3	16.5	41	201
SPS-5	Overlay	51	45	74.3	23.7	31.9	42	119
SPS-5	Overlay	127	48	150.1	25.6	17.1	114	200
SPS-6	Overlay	102	16	103.7	9.0	8.7	86	120
SPS-6	Overlay	203	4	210.0	7.4	3.5	199	215
SPS-8	Surface	102	7	105.3	10.5	10.0	91	119
SPS-8	Surface	178	7	171.2	15.7	9.2	142	187

Note: Standard deviation and COV values reported are for all samples within a given experiment.

Figure 10. Distribution of AC core thickness for base layers.

Figure 10. Distribution of AC core thickness for base layers. The bar graph shows Layer Thickness in millimeters on the horizontal axis, Number of Test on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 1,759, mean = 146 millimeters, stdev = 71 millimeters, and the range = 2.54-371 millimeters.

Figure 11. Distribution of AC core thickness for surface layers.

Figure 11. Distribution of AC core thickness for surface layers. The bar graph shows Layer Thickness in millimeters on the horizontal axis, Number of Test on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 21,477, mean = 73.2 millimeters, stdev = 51 millimeters, and the range = 2.54-406 millimeters.

Figure 12. Distribution of AC core thickness for overlays.

Figure 12. Distribution of AC core thickness for overlays. The bar graph shows Layer Thickness in millimeters on the horizontal axis, Number of Test on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 5,332, mean = 14.4 millimeters, stdev = 14.3 millimeters, and the range = 2.54-343 millimeters.

Assessing AC Core Thickness Data Quality

The first step in assessing AC core thickness data quality was to determine typical variability expected for cores belonging to the same analysis cell. This was done by reviewing typical variability measured as standard deviation or COV in published literature (see table 11). The AASHTO T148 (ASTM C174) test standard provides no recommendations on testing variability, a component of typical variability.

The typical standard deviation for measured core thickness ranges from 5.6 to 26.4 mm, whereas typical COV ranges from 4 to 25 percent. Hughes reported that, for AC core thickness evaluation, variability measured as COV tends to be more stable than standard deviation.⁽¹³⁾ Therefore, COV was adopted as the measure of variability for evaluating data quality. A COV of 20 percent was used as the threshold value for the classification of acceptable and questionable analysis cells.

Figures 13 and 14 present the distribution of COV for the analysis cells evaluated for GPS and SPS experiments. Approximately 87 percent of the analysis cells from the GPS experiments and 86 percent of the analysis cells from SPS experiments had acceptable variability.

Table 11. Typical variability for HMAC- and asphalt-treated layers.^{(13, 21)}

Data Source Layer Type Average Thickness, mm Standard Deviation, mm COV, percent

New Jersey Surface 44 6.6 15.0

New Jersey Surface/binder 57 8.4 14.7

New Jersey Surface/binder 85 10.6 12.5

New Jersey Base 100 14.0 14.0

New Jersey Base 150 14.0 9.3

Kansas DOT¹ Surface/base 112 5.6 5.0

Kansas DOT¹ Surface/base 71 6.6 9.3

Kansas DOT¹ Surface/base 487 19.3 4.0

Kansas DOT¹ Surface/base 67 5.6 8.4

Kansas DOT¹ Surface/base 188 22.1 11.7

Kansas DOT¹ Surface/base 356 6.1 1.7

Kansas DOT¹ Surface/base 272 26.4 9.7

Kansas DOT¹ Surface/base 319 22.6 7.1

Data Source	Layer Type	Average Thickness, mm	Standard Deviation, mm	COV, percent
New Jersey	Surface	44	6.6	15.0
New Jersey	Surface/binder	57	8.4	14.7
New Jersey	Surface/binder	85	10.6	12.5
New Jersey	Base	100	14.0	14.0
New Jersey	Base	150	14.0	9.3
Kansas DOT¹	Surface/base	112	5.6	5.0
Kansas DOT¹	Surface/base	71	6.6	9.3
Kansas DOT¹	Surface/base	487	19.3	4.0
Kansas DOT¹	Surface/base	67	5.6	8.4
Kansas DOT¹	Surface/base	188	22.1	11.7
Kansas DOT¹	Surface/base	356	6.1	1.7
Kansas DOT¹	Surface/base	272	26.4	9.7
Kansas DOT¹	Surface/base	319	22.6	7.1

Average Standard Deviation, mm, 12.9
Average COV, percent, 9.4
¹Consists of core for binder, surface layer, and base layers.

Figure 13. Distribution of COV for analysis cells from GPS experiments.

Figure 14. Distribution of COV for analysis cells from SPS experiments.

Analysis cells classified as questionable were further evaluated to determine the causes of excessive variability. Identified anomalies were rectified where possible or flagged to inform users of the existence of excessive variability.

Identification of Anomalous Data

The focus of the discussion presented so far has been on the data availability and quality. Data availability was assessed at two levels. The anomalies found in the data during the various analyses performed are described below, along with the discussion of possible causes of their occurrence. Corrective or remedial measures taken to address the anomalies are also discussed.

Anomaly 1: Erroneous Material Type

There was some AC core examination and thickness data in table TST_AC01_LAYER with erroneous material description (i.e., nonbituminous materials). Table TST_AC01_LAYER should contain only AC or asphalt-treated material core thickness.

A total of 24 records had erroneous material descriptions--two records from GPS experiments and 22 from SPS experiments. A feedback report was generated and sent to the FHWA for the data in these records to be reassessed and rectified. Meanwhile, the records were retained and used to develop the representative data table. A comment code was assigned to them to explain the anomaly. The FHWA will rectify this anomaly.

Anomaly 2: Excessive Variability

A total of 436 analysis cells had excessive variability in the test data. The breakdown of these data was as follows:

GPS experiments--13.3 percent (195 of the 1,466 analysis cells with multiple test values) had excessive within-cell variability.
SPS experiments--14.4 percent (241 of the 1,673 analysis cells) had excessive within-cell variability.

For analysis cells with excessive variability, potential outliers were identified by:

Considering all test data within an analysis cell that fell outside four standard deviations of the mean as suspicious and, hence, outliers.
Applying the ASTM C178 procedure to identify potential outliers.

Test results classified as outliers were not used in developing the representative data table and summary statistics. All other test data in analysis cells with excessive variability were included in the representative data table. However, a comment code was assigned to such data, explaining the reason for excessive variability, where possible.

For the analysis cells in GPS experiments, no apparent reason for excessive variability was found in 5.3 percent (78 of 1,466) of the cells. For SPS experiments, the percentage of cells with no apparent reason for excessive variability was 7.5 (126 of 1,673). Excessive variability in some analysis cells (56 GPS cells and 74 SPS cells) was caused by noncompliance with test protocols (i.e., measurement of very thin AC cores). Average COV for core specimens from these analysis cells was generally greater than typical. However, these data had a standard deviation of less than 5 mm.

Sampling location in a given test section (i.e., approach vs. end section) had a significant influence on within-cell variability. Approximately 3 percent (42 of 1,466) of GPS cells and 1 percent (14 of 1,673) of SPS cells showed excessive variability, due to significantly different thickness results reported for cores taken from the approach and leave sections of the test pavement. This implies that the layer thickness increased or decreased consistently along the test section. No remedial action was taken to correct such data because the data are not erroneous. Test sections with this anomaly were flagged in the representative data table.

Approximately 2 percent (20 of 1,466) of the GPS cells and 1 percent (19 of 1,673) of the SPS cells had outliers causing excessive variability and, thus, were not used in developing the representative summary statistics data tables.

Anomaly 3: Unreasonable Thickness Values (Outside the Recommended Range)

The LTTP material testing guide provides typical thickness ranges for asphalt-treated materials based on layer type.⁽²⁾ A total of 244 core specimens from GPS experiments had thickness values outside the recommended range. For SPS experiments, 94 core specimens had thickness values outside the recommended thickness range.

No remedial action was taken. However, comment codes were assigned to the analysis cells containing such data in the representative data tables.

Remedial Action--Summary

The implementation of appropriate remedial action resulted in a significant reduction of variability in 39 out of the 436 analysis cells originally exhibiting excessive variability. The 39 cells were reclassified as acceptable. The remaining 397 analysis cells still had excessive variability. They were identified in the representative data table with appropriate comment codes indicating the excessive variability.

Schema of the Representative AC Core Examination and Thickness Data Table (TST_AC01_LAYER_REP)

A representative AC core thickness data table, TST_AC01_LAYER_REP, was developed after addressing the identified anomalies, and it is recommended for inclusion into the LTPP database. The data fields in this table are as follows:

State code.
SHRP identification number.
Layer number.
Construction number.
Number of specimen tested.
Mean thickness.
Maximum thickness.
Minimum thickness.
Standard deviation of thickness data.
COV of thickness data.
QA_Comment_1.
QA_Comment_2.
QA_Comment_3.
QA_Comment_4.
QA_Comment_5.
QA_Comment_6.
QA_Comment_Other.
Data source.
Record status.

The first four fields define the analysis cell used in computing summary statistics of the measured thickness for each analysis cell. Fields 5 through 10 present the representative test results and associated statistics. Comments are included in fields 11 to 17 through describe the quality status of the data (e.g., excessive variability or incomplete sampling), based on the quality assurance analysis performed as part of this study. The comments describe the anomaly types encountered in data evaluation and remedial action implemented to correct them. Anomalies that were not remedied are also identified. The remaining fields describe the source of the data and record status.

5. BULK SPECIFIC GRAVITY OF AC CORES

Introduction

Bulk specific gravity plays a critical role in the design, construction, and quality control of HMAC paving mixtures. It is also a key input in making weight-volume conversions and in calculating the void content in compacted HMAC. Table TST_AC02 contains BSG test results from all relevant LTPP test sections.

Testing is done using guidelines presented in SHRP protocol P02, Bulk Specific Gravity of Asphaltic Concrete and the test standard AASHTO T166, Bulk Specific Gravity of Compacted Bituminous Mixtures Using Saturated Surface-Dry Specimens (Method A). SHRP P02 requires that for test samples with a percent water absorbed greater than 2 percent, AASHTO T166 should be replaced with AASHTO T275, Bulk Specific Gravity of Compacted Bituminous Mixtures Using Paraffin-Coated Specimens (Method A).^{(2, 3, 4)}

The test protocol and standards provide guidance on material sampling, preparation of test specimens, testing, computation of test results, and presentation of results. The test results are stored in the LTPP database after undergoing several levels of quality checks. Data classified at level E are stored in table TST_AC02. Table TST_AC02 has a total of 22 fields of information:

SHRP_ID.
State code.
Layer number.
Field set.
Location number.
Construction number.
Sample number.
Lab code.
Sample area number.
BSG.
Water absorption.
Paraffin coated.
Sample number.
Test date.
Comment 1.
Comment 2.
Comment 3.
Comment 4.
Comment 5.
Comment 6.
Comment other.
Record status.

Material Sampling for Bulk Specific Gravity of AC Cores

Several LTPP documents provide guidelines for material sampling (extraction of core specimens) and handling, laboratory testing, and QA/QC. (See references 2 and 15 through 20.) Testing was performed on 102-mm and 150-mm-diameter cores (4-in and 6-in-diameter cores) extracted from specific locations at the approach and leave sections of the pavement test section. Details of core locations are provided in SHRP P02 and the relevant SPS Guidelines for Nomination and Evaluation of SPS Candidate Projects documents. (See references 2 and 15 through 20.) The extracted cores were packaged, labeled, and shipped to designated SHRP laboratories for testing.

Sampling requirements for BSG testing are presented in tables 12 and 13. The tables show the minimum number of core specimens required per AC layer for the various LTPP experiments, along with the core sampling locations.

Bulk Specific Gravity Data Completeness

Data completeness for AC BSG data in table TST_AC02 was evaluated at two levels. Level 1 data completeness evaluation consisted of the total amount of test data available in table TST_AC02 (levels A to E), the percentage of the data at level E, and the number of analysis cells represented by the data at level E.

For GPS experiments, every AC layer in a test section was considered an analysis cell. Therefore, an analysis cell is defined as a unique combination of State_code, SHRP_ID, and Layer_No.
For SPS experiments, an analysis cell was defined as an AC layer within an SPS test section within an experiment. Therefore, it has a unique combination of State_code, SHRP_ID, Material_Code, and Layer_Type. Table 12 presents a summary of the fields used in defining analysis cells for GPS and SPS experiments.

Level 2 data completeness consisted of determining the percentage of analysis cells with the minimum number of test results required and reported at level E. The minimum number of tests required by LTPP is summarized in tables 13 and 14. The January 2000 update of table TST_AC02 was used in data evaluation and analysis.

Table 12. Data fields used for defining analysis cells for BSG.

Data Fields GPS SPS¹

SHRP_ID X X

State code X X

Layer number X X

Layer description X

Material code X

Data Fields	GPS	SPS¹
SHRP_ID	X	X
State code	X	X
Layer number	X	X
Layer description		X
Material code		X

¹SPS 1, 2, 5, 6, 7, 8, and 9A.

Table 13. Sampling and testing requirements for BSG of AC cores for GPS experiments.

Experiment Type Layer Type LTPP Designation SHRP Protocol Minimum Number of Tests per Layer Sampling Location

GPS-1, -2, -6, and -7 AC AC02 P02 2 A1, A2, C7, C9, C10, C19, C21, C22, (C12, C24 if needed)

Experiment Type	Layer Type	LTPP Designation	SHRP Protocol	Minimum Number of Tests per Layer	Sampling Location
GPS-1, -2, -6, and -7	AC	AC02	P02	2	A1, A2, C7, C9, C10, C19, C21, C22, (C12, C24 if needed)

Table 14. Details of sampling and testing requirements for BSG of AC cores for SPS experiments.

Expt Type Construction Stage LTPP Designation Sections Minimum Number of Tests per Layer Source/Sampling Location

SPS-1 Asphalt-treated base AC02 -- 34 102-mm OD cores C1-C10, C21-C34, C47-C56

SPS-1 AC surface and binder AC02 -- 60 102-mm OD coresC1-C60

SPS-5 Preconstruction AC02 -- 9 C3, C4, C5, [C13,C14,C15], [C22,C23,C24]

SPS-5 Postconstruction AC02 -- 40 All cores

SPS-6 -- AC02 -- 20 All cores

SPS-8 -- AC02 -- 16 All cores

SPS-9 Mix design AC02 01 and 03 3 LA01AXX-LA03AXX

SPS-9 Compacted bulk samples AC02 02 18 LA01A02-LA07LA02, LA15A02, LA38A02, DA02A02,DA03A02, DA04A02, DA06A02, DA16A02, DA22A02, DA31A02, DA32A02, DA33A02

SPS-9 QA test AC02 01 and 03 6 BA01AXX-BA06AXX

Postconstruction AC02 01, 02, and 03 8 CA02tXX, CA06tXX, CA11txx, CA15txx, CA19tXX, CA24tXX, CA28tXX, CA33tXX

Expt Type	Construction Stage	LTPP Designation	Sections	Minimum Number of Tests per Layer	Source/Sampling Location
SPS-1	Asphalt-treated base	AC02	--	34	102-mm OD cores C1-C10, C21-C34, C47-C56
SPS-1	AC surface and binder	AC02	--	60	102-mm OD coresC1-C60
SPS-5	Preconstruction	AC02	--	9	C3, C4, C5, [C13,C14,C15], [C22,C23,C24]
SPS-5	Postconstruction	AC02	--	40	All cores
SPS-6	--	AC02	--	20	All cores
SPS-8	--	AC02	--	16	All cores
SPS-9	Mix design	AC02	01 and 03	3	LA01AXX-LA03AXX
SPS-9	Compacted bulk samples	AC02	02	18	LA01A02-LA07LA02, LA15A02, LA38A02, DA02A02,DA03A02, DA04A02, DA06A02, DA16A02, DA22A02, DA31A02, DA32A02, DA33A02
SPS-9	QA test	AC02	01 and 03	6	BA01AXX-BA06AXX
	Postconstruction	AC02	01, 02, and 03	8	CA02tXX, CA06tXX, CA11txx, CA15txx, CA19tXX, CA24tXX, CA28tXX, CA33tXX

Note: Postconstruction cores to be tested at 0, 6, 12, 18, 24, and 48 months after construction.

Level 1 Data Completeness

The first step in assessing level 1 data completeness was the extraction and assembly of all BSG data from the LTPP database table TST_AC02. The layer and material description information in table TST_AC02 was cross-referenced with similar information in other LTPP tables, such as TST_ LO5B and EXPERIMENT_ SECTION, by combining these tables with TST_AC02. Cross referencing the data made it possible to check for anomalies in materials description, layer type, and layer number information in table TST_AC02. Test results or records with anomalies in material and layer information were further evaluated to determine the causes of the anomalies. The results of the level 1 data completeness analysis are presented in table 15.

The information presented in table 15 shows that TST_AC02 contained a total of 9,016 records. The 992 records representing test data from SPS supplementary sections were excluded from further analysis because their evaluation falls outside of the scope of this study. Approximately 97 percent of the remaining records were at level E.

Table 15. Level 1 data completeness for table TST_AC02.

Experiment Type Experiment No. Total Number of Records at All Levels Total Number of Records at at Level E Percentage of Records at Level E Number of Analysis Cells Represented at Level E

GPS 1 1831 1829 99.89 320

GPS 2 1472 1470 99.86 239

GPS 3 40 40 100 10

GPS 4 6 6 100 2

GPS 5 89 89 100 23

GPS 6A 780 779 99.87 138

GPS 6B 385 378 98.18 92

GPS 6C 28 28 100 6

GPS 6D 12 11 91.67 2

GPS 6S 174 172 98.85 40

GPS 7A 226 226 100 49

GPS 7B 125 125 100 24

GPS 7C 16 16 100 2

GPS 7S 8 8 100 4

GPS 9 22 22 100 7

SPS 1 1035 1010 97.6 42

SPS 5 1190 1079 90.7 54

SPS 6 186 130 69.9 9

SPS 8 100 100 100 9

SPS 9 299 256 85.6 11

Experiment Type	Experiment No.	Total Number of Records at All Levels	Total Number of Records at at Level E	Percentage of Records at Level E	Number of Analysis Cells Represented at Level E
GPS	1	1831	1829	99.89	320
GPS	2	1472	1470	99.86	239
GPS	3	40	40	100	10
GPS	4	6	6	100	2
GPS	5	89	89	100	23
GPS	6A	780	779	99.87	138
GPS	6B	385	378	98.18	92
GPS	6C	28	28	100	6
GPS	6D	12	11	91.67	2
GPS	6S	174	172	98.85	40
GPS	7A	226	226	100	49
GPS	7B	125	125	100	24
GPS	7C	16	16	100	2
GPS	7S	8	8	100	4
GPS	9	22	22	100	7
SPS	1	1035	1010	97.6	42
SPS	5	1190	1079	90.7	54
SPS	6	186	130	69.9	9
SPS	8	100	100	100	9
SPS	9	299	256	85.6	11

Note: There were a total of 992 records from SPS supplemental sections not listed in this table.

Level 2 Data Completeness

The analysis cells were further evaluated to determine whether the minimum number of tests required had been performed and reported at level E. Level 2 data completeness consisted of checking the amount of test records available in each analysis cell and comparing it to the sampling and testing requirements presented in SHRP P02 protocol and other relevant SHRP documents. (See references 2, 15 through 20.) Analysis cells with at least the minimum number of test records required were categorized as complete, whereas analysis cells with less than the minimum required test results at level E were classified as incomplete. Results of level 2 data completeness are presented in table 16.

Table 16. Summary of level 2 data completeness for table TST_AC02.

Experiment Type No. of Analysis Cells with Data Min. Number of Test Results Required No. of Analysis Cells with Min. Number of Test Results Percent Analysis Cells with Minimum Test Results

GPS-1 298 2 293 98.3

GPS-2 234 2 229 97.9

GPS-3 10 2 8 80.0

GPS-4 1 2 1 100

GPS-5 23 2 23 100

GPS-6A 135 2 129 95.6

GPS-6B 92 2 79 85.9

GPS-6C 6 2 6 100

GPS-6D 2 2 1 50.0

GPS-6S 33 2 30 90.9

GPS-7A 45 2 44 97.8

GPS-7B 22 2 22 100

GPS-7C 2 2 2 100

GPS-7S 4 2 4 100

GPS-9 4 2 4 100

SPS-1¹ 24 60/34 2 8.3

SPS-5² 60 9/40 13 22.0

SPS-6 7 20 3 43.0

SPS-8 6 16 3 50.0

Experiment Type	No. of Analysis Cells with Data	Min. Number of Test Results Required	No. of Analysis Cells with Min. Number of Test Results	Percent Analysis Cells with Minimum Test Results
GPS-1	298	2	293	98.3
GPS-2	234	2	229	97.9
GPS-3	10	2	8	80.0
GPS-4	1	2	1	100
GPS-5	23	2	23	100
GPS-6A	135	2	129	95.6
GPS-6B	92	2	79	85.9
GPS-6C	6	2	6	100
GPS-6D	2	2	1	50.0
GPS-6S	33	2	30	90.9
GPS-7A	45	2	44	97.8
GPS-7B	22	2	22	100
GPS-7C	2	2	2	100
GPS-7S	4	2	4	100
GPS-9	4	2	4	100
SPS-1¹	24	60/34	2	8.3
SPS-5²	60	9/40	13	22.0
SPS-6	7	20	3	43.0
SPS-8	6	16	3	50.0

¹For the SPS-1 project, 60 tests are required for the AC surface and binder layers, and 34 tests are required for the asphalt-treated base.
²For the SPS-5 project, nine tests are required on the original AC surface preconstruction, and 40 tests are required on the overlay postconstruction.

Table 16 shows that level 2 data completeness for GPS experiments ranged from 80 to 100 percent; most of the sampling and testing is complete. For SPS test sections, data completeness ranged from 8 to 50 percent, indicating that there is still a significant amount of testing ongoing. This is reasonable because there still are SPS test pavements under construction.

Bulk Specific Gravity of AC Cores Data Quality

Data quality was evaluated for all the data in table TST_AC02. The first step was to evaluate the test data for reasonableness by comparing test values with typical AC BSG test values. The range of typical test values was obtained from published literature.⁽²²⁾ The data were then further evaluated to determine how much variability exists within each analysis cell. The variability was compared with the typical variability and classified as acceptable or questionable.

Data Reasonableness

Bulk specific gravity of a compacted AC mixture is influenced by the following mixture properties:

Aggregate specific gravity.
Specific gravity of the asphalt binder.
Percentage of air voids in the compacted mixture.

In compacted asphalt mixes, about 90 percent of the volume consists of aggregates. Therefore, the specific gravity of aggregates predominantly controls the BSG for the AC mixtures as a whole. Bulk specific gravity values of some of the common types of rocks found in North America and used in AC mixtures are shown in the table 17.⁽²²⁾ Based on the data presented in table 17, a typical range in BSG values of 1.8 to 2.8 was adopted. Most of the BSG test values evaluated were found to be within the typical range of 1.8 to 2.8. The 7 results outside the typical range are listed in table 18. These test results required further evaluation. Feedback reports documenting the possible error in such data were generated and sent to the FHWA for evaluation.

Table 17. Typical values of specific gravity for selected aggregate.⁽²²⁾

Type of Rock Specific Gravity Absorption (percent)

Granite 2.65 0.3

Syenite 2.74 0.4

Diorite 2.92 0.3

Felsite 2.66 1.8

Limestone 2.66 0.9

Dolomite 2.70 1.1

Shale 1.8-2.5 > 1.0

Sandstone 2.54 1.8

Type of Rock	Specific Gravity	Absorption (percent)
Granite	2.65	0.3
Syenite	2.74	0.4
Diorite	2.92	0.3
Felsite	2.66	1.8
Limestone	2.66	0.9
Dolomite	2.70	1.1
Shale	1.8-2.5	> 1.0
Sandstone	2.54	1.8

Table 18. Summary of nontypical BSG test data.

SHRP_ID BSG Possible Anomaly

481119 3.347 Higher than typical test values

481119 3.402 Higher than typical test values

491005 0.49 Lower than typical test values

483669 1.617 Lower than typical test values

483669 1.734 Lower than typical test values

483679 1.554 Lower than typical test values

483679 1.580 Lower than typical test values

SHRP_ID	BSG	Possible Anomaly
481119	3.347	Higher than typical test values
481119	3.402	Higher than typical test values
491005	0.49	Lower than typical test values
483669	1.617	Lower than typical test values
483669	1.734	Lower than typical test values
483679	1.554	Lower than typical test values
483679	1.580	Lower than typical test values

The distributions of BSG test results are presented in figures 15 and 16 for GPS experiments and in figures 17 and 18 for SPS experiments. Approximately 80 percent of the cores tested from GPS experiments had BSG values ranging from 2.20 to 2.40. For SPS cores, approximately 90 percent had BSG values ranging from 2.20 to 2 .60.

Figure 15. Distribution of BSG test results for GPS surface layers.

Figure 15. Distribution of Bulk Specific Gravity (BSG) test results for GPS surface layers. The bar graph shows BSG on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 4,489, mean = 2.35, stdev = 0.10, and the range = 0.45-3.37.

Figure 16. Distribution of BSG test results for GPS base layers.

Figure 16. Distribution of BSG test results for GPS base layers. The bar graph shows BSG on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 308, mean = 2.29, stdev = 0.13, and the range = 1.89-2.62.

Figure 17. Frequency distribution of BSG measurements for all dense-graded HMAC of SPS surface layers.

Figure 17. Frequency distribution of BSG measurements for all dense-graded HMAC of SPS surface layers. The bar graph shows BSG on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 1,596, mean = 2.34, stdev = 0.12, and the range = 2.00-2.64.

Figure 18. Frequency distribution of BSG measurements for all dense-graded HMAC of SPS base layers.

Figure 18. Frequency distribution of BSG measurements for all dense-graded HMAC of SPS base layers. The bar graph shows BSG on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 119, mean = 2.30, stdev = 0.07, and the range = 2.14-2.50.

Assessing Bulk Specific Gravity Data Quality

The first step in assessing BSG data quality was to determine typical variability expected for cores belonging to the same analysis cell. This was done by reviewing typical variability measured as standard deviation or COV in published literature.⁽²¹⁾ The AASHTO T166 test standard recommends a precision (i.e., testing variability) of 0.02 BSG between two test results for a field compacted sample. Assuming a mean BSG value of 2.3, this translates to a COV of 0.9 percent.

There is very little information in published literature on the typical variability of BSG test results. However, there is considerable information on the typical variability of AC air voids. The information available is summarized in table 19.⁽²²⁾ AC air voids variability data was used to augment the data available for BSG, because air voids content is directly related to BSG, and the same levels of variability can be expected for each parameter. The relationship between AC air voids and BSG is as follows:

PAVC = 100 times (MSG minus BSG) over MSG

where:

PAVC = percent air voids content
MSG = maximum specific gravity
BSG = bulk specific gravity

Table 19. Typical variability for air voids.⁽²²⁾

Data Source Method Standard Deviation, percent

California Cores 1.9

New Jersey Cores 1.5

Ontario Cores 1.6

Colorado Cores 1.0

Washington Nuclear 0.9

Virginia Cores 1.3

Data Source	Method	Standard Deviation, percent
California	Cores	1.9
New Jersey	Cores	1.5
Ontario	Cores	1.6
Colorado	Cores	1.0
Washington	Nuclear	0.9
Virginia	Cores	1.3

A COV of 2 percent was adopted as the typical variability expected between BSG test results from a common source. It will be the threshold value for the classification of acceptable and questionable analysis cells. A COV greater than 2 percent is, however, not unusual. It is simply an indicator of poor field compaction. Figures 19 and 20 present the distribution of COV for the GPS and SPS analysis cells, respectively. Approximately 90 percent of the analysis cells from GPS experiments had acceptable within-cell variability (COV less than 2 percent). For SPS experiments, approximately 82 percent had acceptable within-cell variability. The causes of excessive variability and other anomalies were further evaluated and possible remedial action implemented, as described in the next few sections of this chapter.

Identification of Anomalous Data

This discussion has so far focused on data availability and quality assessment. Data quality checks were performed to verify data reasonableness, protocol compliance, and within-cell variability. The anomalies found in the data during the various analyses performed are described in this section, along with possible causes of their occurrence. Where possible, corrective or remedial measures were implemented to address the identified anomalies, as described in the following sections.

Anomaly 1: Erroneous Material Type

Six records in this table had erroneous material type descriptions (nonbituminous material). A feedback report was generated for these records and sent to the FHWA for possible remedial action to be taken. Meanwhile, the records were retained in the representative data table, with a comment code assigned to them.

Figure 19. Distribution of COV of BSG for GPS analysis cells.

Figure 19. Distribution of COV of BSG for GPS analysis cells. The bar graph shows COV in percent on the horizontal axis, Number of Analysis Cells on the left vertical axis, and Cumulative Percentage on the right vertical axis. Analysis Cells with a COV of less than 2% are considered Acceptable and those greater than 2% are Questionable. Over 90% of the GPS Analysis Cells are Acceptable.

Figure 20. Distribution of COV of BSG for SPS analysis cells.

Figure 20. Distribution of COV of BSG for SPS analysis cells. The bar graph shows COV in percent on the horizontal axis, Number of Analysis Cells on the left vertical axis, and Cumulative Percentage on the right vertical axis. Analysis Cells with a COV of less than 2% are considered Acceptable and those greater than 2% are Questionable. Over 80% of the SPS Analysis Cells are Acceptable.

Anomaly 2: Compliance with Test Protocol (Minimum Thickness)

SHRP P02 states that all cores thicker than 38 mm shall be eligible for testing for BSG, and cores less than 38 mm thick (e.g., chip seal, seal coat, surface treatment or patching materials) shall not be tested. The evaluation of the data in table TST_AC02 revealed 24 analysis cells (22 from SPS experiments and two from GPS experiments) contained test results from cores less than 38 mm thick. Test values from such cores are clearly not in compliance with test protocols and were reevaluated for suitability.

Although the testing of cores less than 38 mm is not permitted, they were not removed from the representative data tables unless they were found to be erroneous or outliers. They were, however, assigned proper comment codes in the representative data tables.

Anomaly 3: Excessive Variability

A total of 97 analysis cells (80 from GPS and 17 from SPS experiments) had excessive variability (within-cell variability COV > 2 percent). These analysis cells constitute approximately 10 percent of the total number of analysis cells in table TST_AC02.

Test results outside the mean ± 4 standard deviations were deemed to be suspicious and, hence, were classified as outliers. They were not used for developing the representative data table. Outliers identified using procedures outlined in ASTM 178 were also not used in developing the representative data table. All the remaining data were retained in the representative data tables. A comment code was assigned to the retained data, explaining the possible causes of excessive variability.

Remedial Action--Summary

The individual test records for data within analysis cells with excessive variability were reviewed to determine possible causes of the anomaly. Of the 97 analysis cells with excessive variability, 24 were found to contain two distinct groups of test results within the analysis cell. When evaluated separately, each group within the analysis cell has acceptable variability. Another 10 analysis cells were found to contain suspect test results (test data outside of the mean ± 4 standard deviations). For the remaining 63 analysis cells, no reasonable causes for the anomaly were found.

After taking appropriate remedial action and recomputing the within-cell variability, 10 of the 97 analysis cells originally exhibiting excessive variability were reclassified as acceptable. The remaining 87 analysis cells still had excessive variability. They were retained in the database with appropriate comment codes.

Schema of the Representative Bulk Specific Gravity of AC Cores Tables (TST_AC02_REP_GPS and TST_AC02_REP_SPS)

Representative BSG of AC cores tables, TST_AC02_REP_GPS and TST_AC02_REP_SPS were developed after addressing the identified anomalies, and they are recommended for inclusion into the LTPP database. The data fields in the tables are presented in table 20. The first four fields in the GPS schema and the first six fields in the SPS schema define the analysis cell used in computing summary statistics of the measured BSG values. Comment fields were included to describe the quality status of the data (e.g., excessive variability or incomplete sampling), followed by a field describing the record status.

Table 20. Schema for representative data tables TST_AC02_REP_GPS and TST_AC02_REP_SPS.

Number TST_AC02_REP_GPS TST_AC02_REP_SPS

1 State code State code

2 SHRP identification number SPS cell identification number

3 Layer number Description of Layer

4 Construction number Material code for the layer

5 Number of specimen tested Construction number

6 Mean BSG Number of specimen tested

7 Maximum BSG Mean BSG

8 Minimum BSG Maximum BSG

9 Standard deviation of BSG data Minimum BSG

10 COV of BSG data COV of BSG data

11 QA_Comment_1 Standard deviation of BSG data

12 QA_Comment_2 QA_Comment_1

13 QA_Comment_3 QA_Comment_2

14 QA_Comment_4 QA_Comment_3

15 QA_Comment_5 QA_Comment_4

16 QA_Comment_6 QA_Comment_5

17 QA_Comment_Other QA_Comment_6

18 Record status QA_Comment_Other

19 -- Record status

Number	TST_AC02_REP_GPS	TST_AC02_REP_SPS
1	State code	State code
2	SHRP identification number	SPS cell identification number
3	Layer number	Description of Layer
4	Construction number	Material code for the layer
5	Number of specimen tested	Construction number
6	Mean BSG	Number of specimen tested
7	Maximum BSG	Mean BSG
8	Minimum BSG	Maximum BSG
9	Standard deviation of BSG data	Minimum BSG
10	COV of BSG data	COV of BSG data
11	QA_Comment_1	Standard deviation of BSG data
12	QA_Comment_2	QA_Comment_1
13	QA_Comment_3	QA_Comment_2
14	QA_Comment_4	QA_Comment_3
15	QA_Comment_5	QA_Comment_4
16	QA_Comment_6	QA_Comment_5
17	QA_Comment_Other	QA_Comment_6
18	Record status	QA_Comment_Other
19	--	Record status

6. MAXIMUM SPECIFIC GRAVITY OF ASPHALT CONCRETE

Introduction

Maximum specific gravity, also called Rice specific gravity, is defined as the ratio of the weight in air of a unit volume of an uncompacted bituminous mixture to the weight of an equal volume of gas-free distilled water at a given standard temperature. It is described as the voidless specific gravity of AC mixtures and is one of the important properties of asphalt mixtures. Maximum specific gravity of AC is used in the calculation of the volumetric properties of AC mixtures, including air voids, voids in mineral aggregates, and voids filled with asphalt.

Table TST_AC03 contains the MSG test results for all LTPP test sections. Testing was based on SHRP protocol P03--Maximum Specific Gravity of Asphaltic Concrete--and the test standard AASHTO T209--Maximum Specific Gravity of Bituminous Paving Mixtures (ASTM D2041). The test protocols and standards provide guidance on material sampling, preparation of test specimens, testing of a specimens, and computation and presentation of the test results. After undergoing several levels of QA/QC checks, the test results are stored in table TST_AC03 in the LTPP database. Table TST_AC03 contains the 20 fields of information listed below:

SHRP identification number.
State code.
Layer number.
Field set.
Test number.
Location number.
Construction number.
Lab code.
Sample area number.
MSG.
Sample number.
Test date.
Comments 1.
Comments 2.
Comments 3.
Comments 4.
Comments 5.
Comments 6.
Comments other.
Record status.

Material Sampling for AC Maximum Specific Gravity Testing

Material sampling was performed according to guidelines provided in several LTPP documents and reports, including the SHRP Interim Guide for Laboratory Material Handling and Testing and the SPS Guidelines for Nominations and Evaluation of Candidate Projects. (See references 2 and 15 through 20.) For GPS experiments, test core samples were collected at specific locations outside the distress monitoring sections of the LTPP pavement test sections. For SPS projects, cores were extracted from designated locations adjacent to individual test sections. The core specimens were collected, packaged, labeled, and shipped for testing to the SHRP-designated laboratories according to the procedures described in the SHRP P03 protocol. For SPS experiments, bulk samples were collected both pre- and postconstruction.

Sampling and testing requirements for MSG were used to define analysis cells for GPS and SPS experiments for data quality evaluation (see table 21). In general, an analysis cell was defined as follows:

For GPS experiments, each layer within a test section was considered an analysis cell. An analysis cell was, therefore, defined as a unique combination of State_code, SHRP_ID, and Layer_No.
For SPS experiments, an analysis cell was defined as a unique combination of State_code, SHRP_ID, Material_Code, and Layer_Type.

The sampling and testing requirements are presented in tables 22 and 23.

Data Completeness for Maximum Specific Gravity of AC Cores

MSG data completeness was evaluated at two levels:

Level 1--involved the determination of the total amount of data available in table TST_AC03, the percentage at level E, and the number of analysis cells represented by the level E data.
Level 2--consisted of determining the percentage of analysis cells with the minimum number of tests performed and results reported at level E.

The January 2000 release of table TST_AC03 was used for the analyses.

Table 21. Data fields used for defining analysis cells for BSG.

Data Fields GPS SPS¹

SHRP_ID X X

State code X X

Layer number X X

Layer description X

Material code X

¹SPS 1, 2, 5, 6, 7, 8, and 9A.

Data Fields	GPS	SPS¹
SHRP_ID	X	X
State code	X	X
Layer number	X	X
Layer description		X
Material code		X

Table 22. Sampling and testing requirements for MSG of AC for GPS experiments.

Expt. Type Layer Type LTPP Designation SHRP Protocol Minimum Number of Tests per Layer Sampling Location

GPS-1, -2, -6, and -7 AC AC03 P03 2 A1, A2

Expt. Type	Layer Type	LTPP Designation	SHRP Protocol	Minimum Number of Tests per Layer	Sampling Location
GPS-1, -2, -6, and -7	AC	AC03	P03	2	A1, A2

Table 23. Details of sampling and testing requirements for MSG for SPS experiments.

Expt. Type Construction Stage SHRP Protocol Sections Minimum Number of Tests per Layer Source/Location

SPS-1 Asphalt-treated base P03 -- 3 B19, B20, B21from paver

SPS-1 AC surface and binder layer P03 -- 3 B25, B26, B27from paver

SPS-5 Preconstruction P03 -- 3 BA1-3, TP, BA4-6

SPS-5 Postconstruction P03 -- 6 BV1, BV2, BV3, BR1, BR2, BR3

SPS-6 -- P03 -- 3 BV1, BV2, BV3,

SPS-8 -- P03 -- 3 BV-01, BV-02, BV-03,

SPS-9 Mix design P03 01 and 03 1 NA01AXX

SPS-9 Compacted bulk samples P03 02 3 NA15A02, BA06A02, BA22A02

SPS-9 QA test P03 01 and 03 2 BA02AXX BA04AXX

SPS-9 Postconstruction P03 01, 02, and 03 8 CA02tXX, CA06tXX, CA11txx, CA15txx, CA19tXX, CA24tXX, CA28tXX, CA33tXX

Note: Postconstruction cores to be tested at 0, 6, 12, 18, 24, and 48 months after construction.

Expt. Type	Construction Stage	SHRP Protocol	Sections	Minimum Number of Tests per Layer	Source/Location
SPS-1	Asphalt-treated base	P03	--	3	B19, B20, B21from paver
SPS-1	AC surface and binder layer	P03	--	3	B25, B26, B27from paver
SPS-5	Preconstruction	P03	--	3	BA1-3, TP, BA4-6
SPS-5	Postconstruction	P03	--	6	BV1, BV2, BV3, BR1, BR2, BR3
SPS-6	--	P03	--	3	BV1, BV2, BV3,
SPS-8	--	P03	--	3	BV-01, BV-02, BV-03,
SPS-9	Mix design	P03	01 and 03	1	NA01AXX
SPS-9	Compacted bulk samples	P03	02	3	NA15A02, BA06A02, BA22A02
SPS-9	QA test	P03	01 and 03	2	BA02AXX BA04AXX
SPS-9	Postconstruction	P03	01, 02, and 03	8	CA02tXX, CA06tXX, CA11txx, CA15txx, CA19tXX, CA24tXX, CA28tXX, CA33tXX

Level 1--Data Completeness

The level 1 data completeness evaluation began with the extraction and assembly of the MSG test data from table TST_AC03 in the LTPP database. The layer and material description information in table TST_AC03 was cross-referenced with similar information in other LTPP tables, such as TST_ LO5B and EXPERIMENT_SECTION, by combining these tables with TST_AC03. Cross-referencing the data made it possible to check for anomalies in material description, layer type, and layer number information in table TST_AC03. Test results or records with anomalies in material and layer information were evaluated to determine possible causes of the anomalies. The results of the level 1 data completeness analysis are presented in table 24.

Table 24. Level 1 data completeness for AC03 table.

Expt Type Expt Number Total Number of Records at All Levels Total Number of Records at Level E Percentage of Records at Level E Number of Analysis Cells Represented at Level E

GPS 1 610 608 99.70 306

GPS 2 609 607 99.70 213

GPS 3 6 6 100.00 4

GPS 4 2 2 100.00 1

GPS 6A 257 256 99.60 129

GPS 6B 162 159 98.10 85

GPS 6C 10 10 100.00 5

GPS 6D 4 3 75.00 2

GPS 6S 63 61 96.80 32

GPS 7A 123 123 100.00 49

GPS 7B 52 50 96.20 24

GPS 7C 2 2 100.00 1

GPS 7S 8 8 100.00 4

GPS 9 14 14 100.00 7

SPS 1 124 124 100.00 35

SPS 2 6 6 100.00 1

SPS 5 174 174 100.00 42

SPS 6 66 10 15.20 4

SPS 8 21 21 100.00 9

SPS 9 45 41 91.10 9

Expt Type	Expt Number	Total Number of Records at All Levels	Total Number of Records at Level E	Percentage of Records at Level E	Number of Analysis Cells Represented at Level E
GPS	1	610	608	99.70	306
GPS	2	609	607	99.70	213
GPS	3	6	6	100.00	4
GPS	4	2	2	100.00	1
GPS	6A	257	256	99.60	129
GPS	6B	162	159	98.10	85
GPS	6C	10	10	100.00	5
GPS	6D	4	3	75.00	2
GPS	6S	63	61	96.80	32
GPS	7A	123	123	100.00	49
GPS	7B	52	50	96.20	24
GPS	7C	2	2	100.00	1
GPS	7S	8	8	100.00	4
GPS	9	14	14	100.00	7
SPS	1	124	124	100.00	35
SPS	2	6	6	100.00	1
SPS	5	174	174	100.00	42
SPS	6	66	10	15.20	4
SPS	8	21	21	100.00	9
SPS	9	45	41	91.10	9

Note: There were a total of 78 records from SPS supplemental sections not listed in this table.

There were a total of 2,436 records at (all levels) in table TST_AC03. Approximately 94 percent (2,285 of 2,436) were at level E. The 2,285 records at level E represented 962 analysis cells. Seventy-eight records were from SPS supplementary sections and, thus, removed from further analysis.

Level 2--Data Completeness

The level E data were further evaluated to determine whether the minimum number of tests required had been performed and results reported at level E. This was done by checking the amount of test results or records available in each analysis cell and comparing it with the sampling and testing requirements presented in the SHRP P03 protocol and summarized in tables 21 and 22. Analysis cells with at least the minimum number of test records required were categorized as complete, whereas analysis cells with less than the minimum required test results at level E were classified as incomplete. Results of the level 2 data completeness analysis are presented in table 25.

Table 25. Level 2 data completeness assessment for table TST_AC03.

Experiment Type No. of Analysis Cells with Test Data and Material Information Min. Number of Test Results Required Analysis Cells with Minimum Number of Test Results Percent Analysis Cells with Minimum Test Results

GPS-1 289 2 269 93.10

GPS-2 209 2 200 95.70

GPS-3 4 2 2 50.00

GPS-6A 126 2 118 93.70

GPS-6B 85 2 72 84.70

GPS-6C 5 2 5 100.00

GPS-6D 2 2 1 50.00

GPS-6S 25 2 23 92.00

GPS-7A 45 2 42 93.30

GPS-7B 23 2 23 100.00

GPS-7C 1 2 1 100.00

GPS-7S 4 2 4 100.00

GPS-9 4 2 4 100.00

SPS-1 20 3 18 90.00

SPS-2 1 NR¹ 1 100.00

SPS-5 39 3/6² 10 26.00

SPS-6 3 3 1 33.00

SPS-8 5 3 2 40.00

¹NR -- Not required by the materials testing plan.
²For the SPS-5 project three tests are required on the original AC surface preconstruction, and six tests are required on the overlay postconstruction.

Experiment Type	No. of Analysis Cells with Test Data and Material Information	Min. Number of Test Results Required	Analysis Cells with Minimum Number of Test Results	Percent Analysis Cells with Minimum Test Results
GPS-1	289	2	269	93.10
GPS-2	209	2	200	95.70
GPS-3	4	2	2	50.00
GPS-6A	126	2	118	93.70
GPS-6B	85	2	72	84.70
GPS-6C	5	2	5	100.00
GPS-6D	2	2	1	50.00
GPS-6S	25	2	23	92.00
GPS-7A	45	2	42	93.30
GPS-7B	23	2	23	100.00
GPS-7C	1	2	1	100.00
GPS-7S	4	2	4	100.00
GPS-9	4	2	4	100.00
SPS-1	20	3	18	90.00
SPS-2	1	NR¹	1	100.00
SPS-5	39	3/6²	10	26.00
SPS-6	3	3	1	33.00
SPS-8	5	3	2	40.00

Approximately 80 percent of the analysis cells had the minimum number of tests performed and results reported at level E. Level 2 data completeness for GPS experiments ranged from 50 to 100 percent, whereas that for SPS experiments ranged from 26 to 100 percent. Level 2 data completeness for SPS-5 experiments was particularly low (26 percent), possibly because sampling and testing is still ongoing for some SPS projects.

MSG Data Quality

Data quality checks for MSG data consisted of evaluating the individual test results for reasonableness and evaluating the within-cell variability of test results in table TST_AC03. The data were evaluated for reasonableness by comparing the MSG test values in table TST_AC03 with typical test results in published literature.⁽²¹⁾ Subsequently, the data were evaluated for compliance with the relevant SHRP test protocol. Within-cell variability for the analysis cells with multiple test data was also evaluated for reasonableness by comparing with typical values in the literature.

Data Reasonableness

MSG test values are influenced by the following:

Specific gravity of the coarse and fine aggregate used in the mixture.
Specific gravity of the asphalt binder used in the mixture.

The specific gravity of aggregate materials typically ranges from 1.8 to 2.8, whereas that for asphalt binders is approximately 1.0. AC mixtures typically consist of a 3 to 7 percent by weight AC binder content, with the remainder being aggregates. Because of the low percentage of asphalt binder in AC mixtures, mix specific gravity is dominated by the aggregate specific gravity. Therefore, it is reasonable to assume that the typical range of aggregate specific gravity will not be significantly different from the typical range of specific gravity for the AC mixture as a whole. MSG test results outside the range of 1.8 to 2.8 are, therefore, questionable and were evaluated further; however, it must be noted that test results outside this range are not necessarily erroneous.

Figures 21 and 22 present the distribution of GPS surface and base layer MSG test results for all the records in table TST_AC03 at level E. The figures show that 80 percent (1,397 of 1,746) were within the range of 2.40 to 2.60, 15 percent were within the range of 2.2 to 2.4, and 5 percent were within the range of 2.6 to 2.8. Only four records were found out of the specified typical range of 1.8 to 2.8 percent, and these results were evaluated further to determine their effect on within-cell variability. Test results causing excessive variability were not used in developing the representative test values and summary statistics, as discussed later in this chapter.

Figures 23 and 24 present the distribution of SPS surface and base layer MSG test results for all the records in table TST_AC03 at level E. The figures show that 83 percent (204 of 246) were within the range of 2.40 to 2.60, 12 percent were within the range of 2.2 to 2.4, and 5 percent within the range of 2.6 to 2.8. No records were outside of the specified typical range of 1.8 to 2.8 percent.

Figure 21. Distribution of MSG test results for GPS experiments (surface layers).

Figure 21. Distribution of Maximum Specificity Gravity (MSG) test results for GPS experiments (surface layers). The bar graph shows MSG on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 1666, mean = 2.47, stdev = 0.083, and range = 1.73-2.85.

Figure 22. Distribution of MSG test results for GPS experiments (base layers).

Figure 22. Distribution of MSG test results for GPS experiments (base layers). The bar graph shows MSG on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 80, mean = 2.49, stdev = 0.085, and range = 2.31-2.72.

Figure 23. Distribution of MSG test results for GPS experiments (surface layers).

Figure 23. Distribution of MSG test results for GPS experiments (surface layers). The bar graph shows MSG on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 198, mean = 2.48, stdev = 0.076, and range = 2.28-2.72.

Figure 24. Distribution of MSG test results for SPS experiments (base layers).

Figure 24. Distribution of MSG test results for SPS experiments (base layers). The bar graph shows MSG on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 48, mean = 2.47, stdev = 0.072, and range = 2.35-2.58.

Assessing MSG Data Quality

Data quality assessment began with determining the appropriate typical variability expected in MSG test data obtained from specimens with similar properties and characteristics. Typical variability (as defined in preceding chapters) consists of testing, material, sampling, and construction variability expected from field samples tested in the laboratory. Table 26 presents a summary of testing variability recommended by AASHTO T209.⁽⁴⁾

Table 26. MSG testing recommended variability.⁽⁴⁾

Condition of Test Standard Deviation Acceptable Range for Two Test Results

Single operator (not based on the use of section 7 of AASHTO T209)¹ 0.0004 0.011

Multiple operator (not based on the use of section 7 of AASHTO T209)¹ 0.00064 0.019

Single operator (based on the use of section 7 of AASHTO T209)² 0.00064 0.018

Multiple operator (based on the use of section 7 of AASHTO T209)² 0.0193 0.055

Condition of Test	Standard Deviation	Acceptable Range for Two Test Results
Single operator (not based on the use of section 7 of AASHTO T209)¹	0.0004	0.011
Multiple operator (not based on the use of section 7 of AASHTO T209)¹	0.00064	0.019
Single operator (based on the use of section 7 of AASHTO T209)²	0.00064	0.018
Multiple operator (based on the use of section 7 of AASHTO T209)²	0.0193	0.055

¹Basis of estimate: three replicates, five materials, and five laboratories.
²Basis of estimate: two replicates, seven materials, and ten laboratories.

The test condition in table 26 that matches the LTPP testing conditions (multiple operator testing and applying section 7 of AASHTO T209) had a test standard deviation of 0.0193. However, because typical variability consists of not only testing variability, it will be greater than the AASHTO-recommended testing variability.

MSG test results in table TST_AC03 had a mean of 2.5. The testing standard deviation of 0.0193, therefore, converts to a COV of 0.8 percent. To account for the other sources of variability, a COV of 1 percent was adopted as the threshold value for classifying the within-cell variability as acceptable or questionable. Analysis cells classified as questionable were further evaluated to determine the sources of excessive variability. Remedial actions were implemented, where possible, to correct identified anomalies.

Basic statistics, such as mean, standard deviation, and COV, were computed for all the analysis cells in table TST_AC03 with multiple test data. Figures 25 and 26 show the distribution of variability measured as COV for the analysis cells evaluated. Approximately 91 percent of the analysis cells from the GPS experiments and 94 percent of analysis cells from SPS experiments had acceptable variability (COV < 1 percent).

Figure 25. Distribution of COV for MSG analysis cells from GPS experiments.

Figure 26. Distribution of COV for MSG analysis cells from SPS experiments.

Identification of Anomalous Data

The anomalies identified in the MSG test data and recommended remedial actions are presented in the following sections.

Anomaly 1: Erroneous Data Entry

Six records in table TST_AC03 had erroneous materials type descriptions (i.e., nonbituminous materials). A breakdown of the affected records is as follows:

GPS-6, SHRP_ID 422001 contained two records with material type description--crushed aggregate base.
SPS-1, SHRP_ID 311001 contained four records with material type description--cement-treated base.

It is obvious that MSG cannot be determined for nonbituminous materials, and the entries are erroneous.

A feedback report was generated and sent to the FHWA. The erroneous data were excluded from the representative data table. They will be included at a future date, when the anomaly is rectified by the FHWA.

Anomaly 2: Compliance with Test Protocol (Minimum Thickness)

SHRP P03 states that only cores with a minimum thickness of 38 mm shall be eligible for testing. Cores less than 38 mm thick (e.g., chip seal, seal coat, surface treatment, or patching materials) shall not be tested. The evaluation of the data in table TST_AC03 revealed 18 analysis cells (one from SPS experiments and 17 from GPS experiments), with test results from cores that should not have been tested (less than 38 mm thick). Test results from such cores were clearly not in compliance with test protocols and, hence, their suitability for inclusion in the representative data tables was evaluated.

Such data were evaluated for reasonableness or whether they were outliers and causing excessive variability in their respective analysis cells. Unreasonable test results or outliers were excluded from the representative data tables.

Anomaly 3: Excessive Variability

A total of 79 analysis cells (75 from GPS experiments and four from SPS experiments) had excessive within-cell variability (COV > 1 percent). These analysis cells constitute approximately 9 percent of the total number of analysis cells in table TST_AC02.

Test results outside ± 4 standard deviations of the mean were assumed to be erroneous and not used for developing the representative data table. Appropriate comment codes were assigned to the analysis cells with excessive variability to explain possible causes of the excessive variability. Two of the 79 analysis cells with excessive variability contained erroneous test data (test data outside ± 4 standard deviations of the mean test value for the given analysis cell). No plausible reason for the excessive variability was found in the remaining 77 analysis cells.

Schema of the Revised MSG Data Table TST_AC03_REP

Two data tables, TST_AC03_REP_GPS and TST_AC03_REP_SPS, containing representative MSG of AC for GPS and SPS experiments, respectively, were developed and are recommended for inclusion in the LTPP database. The data fields in the tables are presented in table 27.

The first four fields in the GPS schema and first six fields in SPS schema define the analysis cell used in computing summary statistics of the measured MSG values. Comment fields are included to describe the quality status of the data (e.g., excessive variability and incomplete sampling), followed by a field describing record status.

Table 27. Schema for representative data tables TST_AC02_REP_GPS and TST_AC02_REP_SPS.

Number GPS SPS

1 State code State code

2 SHRP identification number SPS cell identification number

3 Layer number Description of layer

4 Construction number Material code for the layer

5 Number of specimen tested Construction number

6 Mean MSG Number of specimen tested

7 Maximum MSG Mean MSG

8 Minimum MSG Maximum MSG

9 Standard deviation Minimum MSG

10 COV COV

11 QA_Comment_1 Standard deviation

12 QA_Comment_2 QA_Comment_1

13 QA_Comment_3 QA_Comment_2

14 QA_Comment_4 QA_Comment_3

15 QA_Comment_5 QA_Comment_4

16 QA_Comment_6 QA_Comment_5

17 QA_Comment_Other QA_Comment_6

18 Record status QA_Comment_Other

19 -- Record status

Number	GPS	SPS
1	State code	State code
2	SHRP identification number	SPS cell identification number
3	Layer number	Description of layer
4	Construction number	Material code for the layer
5	Number of specimen tested	Construction number
6	Mean MSG	Number of specimen tested
7	Maximum MSG	Mean MSG
8	Minimum MSG	Maximum MSG
9	Standard deviation	Minimum MSG
10	COV	COV
11	QA_Comment_1	Standard deviation
12	QA_Comment_2	QA_Comment_1
13	QA_Comment_3	QA_Comment_2
14	QA_Comment_4	QA_Comment_3
15	QA_Comment_5	QA_Comment_4
16	QA_Comment_6	QA_Comment_5
17	QA_Comment_Other	QA_Comment_6
18	Record status	QA_Comment_Other
19	--	Record status

7. ASPHALT CONTENT OF ASPHALTIC CONCRETE

Introduction

Asphalt content of an AC mixture is a very important factor ensuring satisfactory performance. AC mixtures with low asphalt contents are generally less durable than mixtures with optimum asphalt contents, and mixtures with high asphalt contents are generally less stable. Asphalt content directly affects mixture properties such as:

Asphalt film thickness
Voids
Stability
Flow

Asphalt content of a mixture is, therefore, essential for design and research into the behavior of AC materials.

Asphalt content of a mixture is measured by an extraction test. For the LTPP material characterization program, the test protocol and test standard adopted were SHRP protocol P04 and AASHTO T164 (ASTM D2172)--Quantitative Extraction of Bitumen from Bituminous Paving Mixtures.^(2,3) Asphalt content of mixtures is measured as percentage by weight of the total mix. The test data are stored in table TST_AC04 in the LTPP database. Table TST_AC04 has the following 20 fields of information:

SHRP identification number.
State code.
Layer number.
Field set.
Test number.
Location number.
Construction number.
Lab code.
Sample area number.
Mean asphalt content.
Sample number.
Test date.
Comments 1.
Comments 2.
Comments 3.
Comments 4.
Comments 5.
Comments 6.
Comments other.
Record status.

Material Sampling for Asphalt Content of AC Mixtures

SHRP P04 and AASHTO T164 provide guidance on material sampling, preparation of test specimens, testing of specimens, computation of test results, and presentation of results. For test sections in GPS experiments, testing is performed on 300-mm-diameter core specimens retrieved from the approach and leave ends of the test pavement's monitoring section. Testing is also performed on block samples taken from test pits at the leave section of the test pavement. For SPS experiments, core specimens are extracted from specific locations within the entire SPS experiment (between adjacent projects). Detailed information on sample locations and core descriptions for SPS experiments may be found in several SHRP documents. (See references 2 and 15 through 20.)

Table 28 presents the sampling and testing requirements for the determination of asphalt content for GPS test pavements. A detailed summary of SPS sampling requirements is presented in table 29.

Table 28. Sampling and testing requirements for extracted asphalt content.

Expt. Type Layer Type SHRP Protocol Minimum Number of Tests per Layer Sampling Location

GPS-1, -2, -6, and -7 AC P04 2 TP, BA1

Expt. Type	Layer Type	SHRP Protocol	Minimum Number of Tests per Layer	Sampling Location
GPS-1, -2, -6, and -7	AC	P04	2	TP, BA1

Table 29. Sampling and testing requirements for extracted asphalt content for SPS projects.

Expt Type Construction Stage SHRP Protocol Sections Min. No. of Tests per Layer Source/Location

SPS-1 Asphalt-treated base P04 -- 3 B19, B20, B21from paver

SPS-1 AC surface and binder layer P04 -- 3 B25, B26, B27from paver

SPS-2 Asphalt-treated base P04 -- 3 B16 to B18from paver

SPS-5 Preconstrution P04 -- 3 BA1-3, TP, BA4-6

SPS-5 Postconstrution P04 -- 6 BV1, BV2, BV3, BR1, BR2, BR3

SPS-6 -- P04 -- 3 BV1, BV2, BV3,

SPS-8 -- P04 -- 3 BV-01, BV-02, BV-03,

SPS-9 Mix design P04 01 and 03 0 --

SPS-9 Compacted bulk samples P04 02 6 BA01A02, BA06A02, BA11A02, BA16A02, BA22A02, BA34A02

QA test P04 01 and 03 2 BA02AXX-BA04AXX

SPS-9 Post construction P04 01, 02, and 03 8 CA02tXX, CA06tXX, CA11txx, CA15txx, CA19tXX, CA24tXX, CA28tXX, CA33tXX

Expt Type	Construction Stage	SHRP Protocol	Sections	Min. No. of Tests per Layer	Source/Location
SPS-1	Asphalt-treated base	P04	--	3	B19, B20, B21from paver
SPS-1	AC surface and binder layer	P04	--	3	B25, B26, B27from paver
SPS-2	Asphalt-treated base	P04	--	3	B16 to B18from paver
SPS-5	Preconstrution	P04	--	3	BA1-3, TP, BA4-6
SPS-5	Postconstrution	P04	--	6	BV1, BV2, BV3, BR1, BR2, BR3
SPS-6	--	P04	--	3	BV1, BV2, BV3,
SPS-8	--	P04	--	3	BV-01, BV-02, BV-03,
SPS-9	Mix design	P04	01 and 03	0	--
SPS-9	Compacted bulk samples	P04	02	6	BA01A02, BA06A02, BA11A02, BA16A02, BA22A02, BA34A02
	QA test	P04	01 and 03	2	BA02AXX-BA04AXX
SPS-9	Post construction	P04	01, 02, and 03	8	CA02tXX, CA06tXX, CA11txx, CA15txx, CA19tXX, CA24tXX, CA28tXX, CA33tXX

Data Completeness for Asphalt Content of AC Mixtures

Data completeness was evaluated at two levels. Level 1 data completeness consisted of assessing the total number of test records in table TST_AC04, the percentage at level E, and the number of analysis cells represented by the data at level E. For level 2 data completeness, the number of test results in each analysis cell was checked against the minimum required. Cells with at least the minimum required number of tests were classified as complete, whereas those with less than the minimum were classified as incomplete. The January 2000 release of table TST_AC04 was used in the analysis. Analysis cells are defined for GPS and SPS experiments, using the fields presented in table 30.

Table 30. Data fields used for defining analysis cells for asphalt content.

Key Fields GPS SPS¹

SHRP_ID X X

State code X X

Layer number X X

Construction number X X

Layer type X

Material code X

¹SPS-1, -2, -5, -6, -7, -8, and -9A.

Key Fields	GPS	SPS¹
SHRP_ID	X	X
State code	X	X
Layer number	X	X
Construction number	X	X
Layer type		X
Material code		X

Level 1--Data Completeness

The data in table TST_AC04 were extracted and assembled from the LTPP database and checked for level 1 data completeness. Data assembly involved merging table TST_AC04 with other tables, such as TST_L05B and EXPERIMENT_SECTION. Important material and layer description information from all the merged tables was cross-referenced to check for accuracy and consistency. The merged data set was then evaluated for level 1 data completeness, as follows:

Determine all available data in table TST_AC04 (levels A to E).
Determine the percentage of the test data at level E.
Determine the number of analysis cells represented by the data at level E.

Approximately 98.2 percent (2,447 of 2,562) of the asphalt content data in table TST_AC04 was at level E. One hundred records were from SPS supplementary sections and, therefore, removed from any further analysis. Table 31 presents a summary of the level 1 data completeness results.

Table 31. Summary of level 1 data completeness evaluation for asphalt content.

Expt Type Expt Number Total Number of Records at All Levels Total Number of Records at Level E Percentage of Records at Level E Number of Analysis Cells Represented at Level E

GPS 1 602 599 99.50 302

GPS 2 607 607 100.00 214

GPS 3 5 5 100.00 3

GPS 4 2 2 100.00 1

GPS 6A 262 261 99.60 133

GPS 6B 189 187 98.90 84

GPS 6C 16 16 100.00 6

GPS 6D 4 3 75.00 2

GPS 6S 77 77 100.00 23

GPS 7A 118 118 100.00 49

GPS 7B 52 49 94.20 23

GPS 7C 2 2 100.00 1

GPS 7S 12 12 100.00 4

GPS 9 12 12 100.00 7

SPS 1 141 136 96.50 42

SPS 2 28 28 100.00 12

SPS 5 181 181 100.00 40

SPS 6 6 6 100.00 3

SPS 8 21 21 100.00 9

SPS 9A 125 125 100.00 10

Expt Type	Expt Number	Total Number of Records at All Levels	Total Number of Records at Level E	Percentage of Records at Level E	Number of Analysis Cells Represented at Level E
GPS	1	602	599	99.50	302
GPS	2	607	607	100.00	214
GPS	3	5	5	100.00	3
GPS	4	2	2	100.00	1
GPS	6A	262	261	99.60	133
GPS	6B	189	187	98.90	84
GPS	6C	16	16	100.00	6
GPS	6D	4	3	75.00	2
GPS	6S	77	77	100.00	23
GPS	7A	118	118	100.00	49
GPS	7B	52	49	94.20	23
GPS	7C	2	2	100.00	1
GPS	7S	12	12	100.00	4
GPS	9	12	12	100.00	7
SPS	1	141	136	96.50	42
SPS	2	28	28	100.00	12
SPS	5	181	181	100.00	40
SPS	6	6	6	100.00	3
SPS	8	21	21	100.00	9
SPS	9A	125	125	100.00	10

Level 2--Data Completeness

The analysis cells were further analyzed for data completeness by comparing the number of tests required by SHRP with the actual number of test data available in each analysis cell. Analysis cells with at least the minimum number of test data required were classified as complete. Analysis cells with less than the minimum required test data were classified as incomplete. Approximately 72 percent of the analysis cells were classified as complete. The remaining 28 percent were incomplete and were further evaluated to check for possible bias and other anomalies. The checks performed are presented throughout the remainder of this chapter. A summary of level 2 data completeness is presented in table 32.

Asphalt Content Data Quality Evaluation

The asphalt content data in the table TST_AC04 were evaluated for reasonableness and quality. Assessing data reasonableness consisted of comparing individual test results with typical test values. Test data that were within the range of typical values were classified as reasonable. All other data were classified as questionable and required further evaluation. The test data grouped as analysis cells were also assessed for quality by computing within-cell variability. The computed variability was compared with typical variability expected from field data and laboratory testing.

Table 32. Level 2 data completeness from asphalt content data.

Expt. Type No. of Analysis Cells with Test Data and Material Information Min. Number of Test Results Required Analysis Cells with Minimum Number of Test Results Percent Analysis Cells with Minimum Test Results

GPS-1 288 2 271 94.10

GPS-2 208 2 201 96.60

GPS-3 3 2 2 66.70

GPS-6A 129 2 126 97.70

GPS-6B 84 2 74 88.10

GPS-6C 6 2 6 100.00

GPS-6D 2 2 1 50.00

GPS-6S 18 2 18 100.00

GPS-7A 45 2 41 1.10

GPS-7B 22 2 21 95.50

GPS-7C 1 2 1 100.00

GPS-7S 4 2 4 100.00

GPS-9 4 2 2 50.00

SPS-1 31 3 23 74.00

SPS-2 7 NR¹ 6 86.00

SPS-5 36 3/6² 11 31.00

SPS-6 3 3 1 33.00

SPS-8 5 3 2 40.00

Expt. Type	No. of Analysis Cells with Test Data and Material Information	Min. Number of Test Results Required	Analysis Cells with Minimum Number of Test Results	Percent Analysis Cells with Minimum Test Results
GPS-1	288	2	271	94.10
GPS-2	208	2	201	96.60
GPS-3	3	2	2	66.70
GPS-6A	129	2	126	97.70
GPS-6B	84	2	74	88.10
GPS-6C	6	2	6	100.00
GPS-6D	2	2	1	50.00
GPS-6S	18	2	18	100.00
GPS-7A	45	2	41	1.10
GPS-7B	22	2	21	95.50
GPS-7C	1	2	1	100.00
GPS-7S	4	2	4	100.00
GPS-9	4	2	2	50.00
SPS-1	31	3	23	74.00
SPS-2	7	NR¹	6	86.00
SPS-5	36	3/6²	11	31.00
SPS-6	3	3	1	33.00
SPS-8	5	3	2	40.00

¹NR--Not required by the materials testing plan.
²For the SPS-5 projects, three tests are required on the original AC surface preconstruction, and six tests are required on the overlay postconstruction.

Analysis cells with variability equal to or less than typical were classified as acceptable, whereas those with variability greater than typical were classified as questionable. A detailed summary of the data quality evaluation is presented in the next few sections.

Data Reasonableness

The percentage of asphalt content in a mixture depends on several variables, including:

Aggregate gradation.
Aggregate porosity.
Mixture type.

The AC content of dense-graded AC mixtures and stone matrix asphalt (SMA) mixtures typically ranges from 3 to 7 percent.⁽¹³⁾ The typical range for large-stone asphalt mixtures and asphalt-treated bases could be considerably less than 4 percent. A range of 3 to 7 percent was selected as the typical range expected for the surface and base layers and used in assessing data reasonableness. The data were grouped into four categories for evaluation, as follows:

GPS surface layer AC materials.
SPS surface layer AC materials.
GPS base layer AC materials.
SPS base layer AC materials.

Figures 27 and 28 illustrate the distribution of asphalt content for GPS and SPS surface layer AC materials. Approximately 98 percent of the GPS surface layer AC materials had an asphalt content ranging from 3 to 7 percent. For SPS surface layer AC materials, figure 28 shows a range of 2.4 to 7.9 percent. Approximately 97 percent of the asphalt content data fell within the typical range of 3 to 7 percent. Only 2 percent of the test results from GPS surface layers and 3 percent from the SPS surface layers are out of the typical range. Test data outside the typical range were evaluated further to determine their effect on within-cell variability, which is presented later in this chapter.

The distributions of asphalt content for AC base layers for GPS and SPS experiments are presented in figures 29 and 30. The data presented in the figures are summarized as follows:

Approximately 93 percent of the test results from GPS experiments fell within the typical ranges of 3 to 7 percent. Asphalt content ranged from 2.7 to 7.4 percent.
Approximately 90 percent of the test results from SPS experiments fell within the typical ranges of 3 to 6 percent. Asphalt content ranged from 1.8 to 6.3 percent.

Even though a majority of the asphalt content values fell within the typical range, some test values from the SPS experiments were significantly less than the minimum of the typical range (2.0 to 2.5 percent). Such data were evaluated further to determine their effect on within-cell variability, which is presented later in this chapter.

Assessing Asphalt Content Data Quality

Several studies have been conducted to determine the typical variability between AC content test results from core specimens. (See references 11 through 16.) SHRP protocol P04 and AASHTO T164 (ASTM D2172) provide guidance on the variability expected from testing.^(2,3,4) The recommended testing standard deviation for core specimens tested at multiple laboratories was 0.22 percent. The typical variability observed from field data (i.e., sampling, the material's natural variability, and construction variability) is summarized in table 33.⁽¹³⁾

Figure 27. Distribution of asphalt content for HMAC surface material for GPS experiments.

Figure 27. Distribution of asphalt content for HMAC surface materials for GPS experiments. The bar graph shows Mean Asphalt Content in percent on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 1,703, mean = 5.12, stdev = 0.082, and range = 2.6-9.0.

Figure 28. Distribution of asphalt content for HMAC surface material for SPS experiments.

Figure 28. Distribution of asphalt content for HMAC surface materials for SPS experiments. The bar graph shows Mean Asphalt Content in percent on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 81, mean = 4.37, stdev = 0.74, and range = 2.7-7.4.

Figure 29. Distribution of asphalt content measurements for HMAC base layers from GPS experiments.

Figure 29. Distribution of asphalt content measurements for HMAC base layers from GPS experiments. The bar graph shows Mean Asphalt Content in percent on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 81, mean = 4.37, stdev = 0.74, and range = 2.7-7.4.

Figure 30. Distribution of asphalt content measurements for HMAC base layers from SPS experiments.

Figure 30. Distribution of asphalt content measurements for HMAC base layers from SPS experiments. The bar graph shows Mean Asphalt Content in percent on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 30, mean = 4.54, stdev = 1.0, and range = 2.2-6.3.F

Table 33. Summary of typical variability in asphalt content field data.⁽¹³⁾

Data Source Year Test Method Standard Deviation, percent

Virginia 1994 Extraction 0.18

Virginia 1994 Nuclear 0.21

NCAT 1994 Nuclear 0.19

NCAT 1994 Centrifuge 0.44

Washington 1993 Extraction 0.24

Colorado 1993 Extraction 0.15

Pennsylvania 1980 Extraction 0.25

Data Source	Year	Test Method	Standard Deviation, percent
Virginia	1994	Extraction	0.18
Virginia	1994	Nuclear	0.21
NCAT	1994	Nuclear	0.19
NCAT	1994	Centrifuge	0.44
Washington	1993	Extraction	0.24
Colorado	1993	Extraction	0.15
Pennsylvania	1980	Extraction	0.25

Based on the information presented in table 33, a standard deviation of 0.5 percent was adopted. This translates to a COV of 10 percent (assuming a mean asphalt content of 5.0). An analysis cell with a COV greater than 10 percent was, therefore, classified as questionable, whereas a COV of less than or equal to 10 percent was classified as acceptable.

Figures 31 and 32 present the distribution of COV for analysis cells from GPS and SPS experiments. Approximately 88 percent and 77 percent of the analysis cells from the GPS and SPS experiments, respectively, were acceptable.

Identification of Anomalous Data

Several anomalies were identified in the checks described in the preceding sections of this chapter. The anomalies and remedial actions implemented to rectify them are presented in the next few sections.

Anomaly 1: Erroneous Data Entry

There were six records in table TST_AC04 with erroneous material type descriptions (nonbituminous material descriptions). Because it is not possible to obtain asphalt content test results from nonbituminous materials, these entries are erroneous. Feedback reports documenting possibly erroneous entries of material data types were sent to the FHWA for appropriate remedial action. Meanwhile, the records were retained in the database with a comment code assigned to them.

Anomaly 2: Noncompliance with Test Protocol (Minimum Thickness of Testable Layer)

The SHRP Interim Guide for Laboratory Material Handling and Testing states that AC core specimens with a thickness less than 38 mm shall not be tested for asphalt content.⁽²⁾ Table TST_AC04 contains a total of 17 analysis cells (one from SPS experiments and 16 from GPS experiments) with test results from cores that were less than the minimum required thickness.

Figure 31. Distribution of COV of asphalt content analysis cells from GPS experiments.

Figure 32. Distribution of COV of asphalt content analysis cells from SPS experiments.

Test values resulting from testing that was not in compliance with test protocols (cores less than 38 mm thick) were evaluated for reasonableness and whether they were outliers causing excessive variability in their respective analysis cells. Unreasonable test results or outliers were excluded from the development of the representative data tables.

Anomaly 3: Excessive Variability

For this test table, analysis cells were classified as questionable if they had a variability (COV) greater than 10 percent. Approximately 12 percent (99 of 814) of the analysis cells with multiple test results from the GPS experiments had excessive variability. For SPS experiments, 23 percent (19 of 82) of the analysis cells had excessive variability.

All supporting information provided in table TST_AC04, such as comments about the testing process and materials sampling, was carefully reviewed to determine possible causes of the excessive variability. Individual test results were checked for compliance with test protocols, and those with anomalies (such as noncompliance with the test protocol) were not used in the development of the representative data table. Variability and other statistics were recomputed, and comments were provided to describe the remedial action taken. For analysis cells with no plausible reason for excessive variability, all the individual test results were retained; however, comments were provided to indicate excessive variability.

Schema of the Representative Asphalt Content of AC Data Tables (TST_AC04_REP_GPS and TST_AC04_REP_SPS)

Two tables, TST_AC04_REP_GPS and TST_AC04_REP_SPS, containing representative test values of asphalt content of AC for GPS and SPS experiments, respectively, were developed and are recommended for inclusion into the LTPP database. The schema for the new data table is presented in table 34. The first four fields in the GPS schema and first six fields in SPS schema define the analysis cell used in computing summary statistics of the measured asphalt content test values. This is followed by a description of the test results and basic statistics. Comment fields are provided to describe the quality status of the data (e.g., excessive variability and incomplete sampling), followed by a field describing the record status.

Table 34. Schema for tables TST_AC04_REP_GPS and TST_AC04_REP_SPS.

Number GPS SPS

1 State code State code

2 SHRP identification number SPS cell identification number

3 Layer number Description of layer

4 Construction number Material code for the layer

5 Number of specimen tested Construction number

6 Mean asphalt content Number of specimen tested

7 Maximum asphalt content Mean asphalt content

8 Minimum asphalt content Maximum asphalt content

9 Standard deviation of asphalt content Minimum asphalt content

10 COV of asphalt content COV of asphalt content

11 QA_Comment_1 Standard deviation of asphalt content

12 QA_Comment_2 QA_Comment_1

13 QA_Comment_3 QA_Comment_2

14 QA_Comment_4 QA_Comment_3

15 QA_Comment_5 QA_Comment_4

16 QA_Comment_6 QA_Comment_5

17 QA_Comment_Other QA_Comment_6

18 Record status QA_Comment_Other

19 -- Record status

Number	GPS	SPS
1	State code	State code
2	SHRP identification number	SPS cell identification number
3	Layer number	Description of layer
4	Construction number	Material code for the layer
5	Number of specimen tested	Construction number
6	Mean asphalt content	Number of specimen tested
7	Maximum asphalt content	Mean asphalt content
8	Minimum asphalt content	Maximum asphalt content
9	Standard deviation of asphalt content	Minimum asphalt content
10	COV of asphalt content	COV of asphalt content
11	QA_Comment_1	Standard deviation of asphalt content
12	QA_Comment_2	QA_Comment_1
13	QA_Comment_3	QA_Comment_2
14	QA_Comment_4	QA_Comment_3
15	QA_Comment_5	QA_Comment_4
16	QA_Comment_6	QA_Comment_5
17	QA_Comment_Other	QA_Comment_6
18	Record status	QA_Comment_Other
19	--	Record status

8. MOISTURE SUSCEPTIBILITY OF ASPHALT CONCRETE

Introduction

AC mixture properties can be influenced significantly by moisture. Key properties that determine the effect of moisture on mix properties are porosity and aggregate--asphalt interaction. Good adhesion between the asphalt and aggregate is particularly important for mixtures exposed to moisture for prolonged periods because the moisture may compete successfully with the asphalt binder for adsorption onto the aggregate surface, resulting in aggregate--asphalt separation or stripping.

Stripping, which causes mixture disintegration and eventual failure of the pavement, is evaluated by determining the moisture susceptibility of the mixture. For the LTPP test pavements, AC moisture susceptibility is evaluated using SHRP protocol P05 and the test standard AASHTO T283--Resistance of Compacted Bituminous Mixture to Moisture Induced Damage (ASTM D4867--Standard Test Method for Effect of Moisture on Asphalt Concrete Paving Mixtures).^{(2, 3, 4)}

For this test method, moisture susceptibility is evaluated by comparing the indirect tensile strength of control and conditioned laboratory compacted AC cylinders with similar properties. The test cylinders were conditioned by subjecting them to vacuum saturation at specified temperatures. A significant difference in the mean indirect tensile strengths of the control and test specimens implies that the mixture is moisture susceptible and will be adversely affected by prolonged exposure to moisture. The difference in the conditioned and control indirect tensile strengths is characterized by computing the parameter tensile strength ratio (TSR).

The TSR is an indicator of the resistance of the asphalt mix to moisture and is the ratio of the average tensile strength of moisture-conditioned specimens to the average tensile strength of dry specimens of asphalt concrete. High TSR values (values closer to the maximum of 1.0) indicate a higher resistance of the AC mixture to moisture damage. A TSR value greater than 0.8 is generally acceptable for pavement design. TSR is defined as follows:

Equation 8: TSR = average tensile strength of moisture conditioned specimens over average tensile strength of dry specimens.

The test protocols and standards provide guidance on material sampling, preparation of test specimens, testing of specimens, computation of test results, and presentation of results. The test results are stored in the table TST_AC05 in the LTPP database after undergoing several levels of quality checks. Table TST_AC05 contains the following fields:

SHRP identification number.
State code.
Layer number.
Field set.
Location number.
Construction number.
Sample number.
Lab code.
Sample area number.
Method of compaction.
MSG.
Sample no. (six fields).
Sample height (six fields).
Sample diameter (six fields).
BSG after molding (six fields).
Percent air voids (six fields).
BSG after vacuum saturation (three fields).
Maximum load (six fields).
Tensile strength (six fields).
Average tensile strength unconditioned.
Average tensile strength conditioned.
TSR.
Relative variation.
Coarse aggregate stripped.
Fine aggregate stripped.
Test date.
Comments (six fields).
Test date.
Other comments.
Record status.

Overview of Moisture Susceptibility Test Methods

There are several types of AC moisture susceptibility tests currently being used by highway agencies throughout the U.S. The most common are:⁽²³⁾

Tensile ratio test (ASTM D4867).
Visual rating.
Boiling water rating.
Water susceptibility cycles.

Of the four methods listed, one the visual rating is not a true test method. It is part of ASTM D4867 and requires a technician to evaluate moisture damage visually following conditioning and testing. To date, a good correlation between visual rating and tensile strength has not yet been found, making visual rating as a stand-alone moisture susceptibility test suspect.⁽²³⁾

The boiling water test rating (ASTM D3625) for AC moisture susceptibility also does not correlate well with AC tensile strength. This makes the test procedure unreliable and not applicable until a better procedure can be found for interpreting test results.⁽²³⁾

The main limitation to the water susceptibility test method for determining AC moisture susceptibility is estimating the surface area of the aggregates in the AC mix (especially for mixtures with additives). This is because, in most situations, the actual mix gradation used to estimate surface area is significantly different from the design mix gradation. Until a more efficient methodology can be found for estimating aggregate surface area, this test method will not be reliable.⁽²³⁾

Currently, ASTM D4867 or AASHTO T283 is the most widely used method for determining HMA moisture susceptibility. However, many state highway agencies have reported only mixed success with the method. Several research projects have dealt with its shortcomings, resulting in suggested "fixes," but the method remains empirical and liable to give either false positives or false negatives in the prediction of moisture damage. One such study was the SHRP Asphalt Research Program. It extensively investigated fundamental mechanisms of moisture damage and developed new methods for its prediction. The Environmental Conditioning System (ECS; originally AASHTO TP34, Determining Moisture Sensitivity of Compacted Bituminous Mixtures Subjected to Hot and Cold Climate Conditions) was designed to determine the moisture susceptibility of compacted HMA specimens under realistic conditions of temperature, moisture saturation, and dynamic loading found in actual pavements.⁽²³⁾

The ECS test showed promise, but the visual stripping, permeability, and modulus procedures used in TP34 to evaluate moisture damage gave results that were not significantly more precise or accurate than those of AASHTO T283. For this reason, AASHTO T283 was retained in the Superpave mix design method to evaluate HMA moisture susceptibility. AASHTO T283 was also adopted by the LTPP as the test method for assessing AS moisture susceptibility and, hence, damage.

There are several ongoing or recently completed research studies investigating possible new testing methods for AC moisture susceptibility. NCHRP Project 09-19, Superpave Support and Performance Models Management, is one such study, and it recommended several new simple performance tests (SPT's) for asphalt mixes. Combining one or more of these tests with the conditioning procedure in the original ECS method may offer an enhanced ability to predict HMA moisture damage potential. When an improved test is developed and accepted, there may be a need to modify existing moisture susceptibility test results in the LTPP database.

Material Sampling for AC Moisture Susceptibility Testing

This test was required on asphalt concrete samples from SPS experiments only. SHRP Interim Guide for Laboratory Material Handling and Testing provides detailed information on material sampling.⁽²⁾ Samples are collected as directed by the SHRP P05 protocol. Test specimens are retrieved from the paver during construction from the designated locations (i.e., approach, leave, and midsections of the pavement test section). The sample materials were collected during the paving operation to ensure that sampling does not adversely affect the test pavement. The materials retrieved are compacted to simulate field conditions before testing. (See reference 15 through 19.) Table 35 presents a summary of the sampling and testing requirements for moisture susceptibility testing for AC mixtures.

Table 35. Sampling and testing requirements for moisture susceptibility of bituminous mixtures.

Experiment Type Layer Type LTPP Designation SHRP Protocol Minimum Number of Tests per Layer Sampling Location

SPS-1 Asphalt treated base AC05 P05 3 B19, B20, B21 from paver

SPS-1 AC surface and binder AC05 P05 3 B25, B26, B27from paver

SPS-5 (Postconstruction) AC AC05 P05 6 BV1, BV2, BV3, BR1, BR2, BR3

SPS-6 AC AC05 P05 3 BV1, BV2, BV3,

SPS-8 AC AC05 P05 3 BV-01, BV-02, BV-03,

Experiment Type	Layer Type	LTPP Designation	SHRP Protocol	Minimum Number of Tests per Layer	Sampling Location
SPS-1	Asphalt treated base	AC05	P05	3	B19, B20, B21 from paver
SPS-1	AC surface and binder	AC05	P05	3	B25, B26, B27from paver
SPS-5 (Postconstruction)	AC	AC05	P05	6	BV1, BV2, BV3, BR1, BR2, BR3
SPS-6	AC	AC05	P05	3	BV1, BV2, BV3,
SPS-8	AC	AC05	P05	3	BV-01, BV-02, BV-03,

Information in this table was used to define analysis cells for data evaluation (i.e., unique combination of State_Code, SHRP_ID, and Material_Code). Table 36 presents the fields used in defining analysis cells for the various SPS experiments.

Table 36. Data fields used for defining analysis cells for AC moisture susceptibility.

Key fields GPS SPS¹

SHRP_ID X X

State code X X

Layer number X X

Construction number X X

Layer type X

Material code X

¹SPS-1, -2, -5, -6, -7, -8, and -9A

Key fields	GPS	SPS¹
SHRP_ID	X	X
State code	X	X
Layer number	X	X
Construction number	X	X
Layer type		X
Material code		X

Data Completeness for Moisture Susceptibility Data

Data completeness was evaluated at two levels. Level 1 data completeness consisted of determining all the test data in table TST_AC05 (levels A through E), the percentage at level E, and the number of analysis cells represented by the data at level E. Level 2 data completeness consisted of checking all the individual analysis cells to determine whether the minimum required testing has been performed and the results presented at level E. The January 2000 release of table TST_AC05 was used in the analysis.

Level 1--Data Completeness

Moisture susceptibility test data for SPS experiments in table TST_AC05 were extracted from the LTPP database. The extracted data were merged with other data tables with material and layer descrpition information, such as TST_ LO5B and EXPERIMENT_SECTION. The accuracy of the materials and layer information in table TST_AC05 was assessed by cross-referencing it with similar information in the merged table. Test data with anomalies in material and layer description were identified for further evaluation. The results of the level 1 data completeness analysis is presented in table 37.

There were a total of 133 records (at all levels) of data in table TST_AC05. Of these, approximately 55 percent (63 of 133 records) had TSR test values at level E. Also, most of the supporting data, such as asphalt content in table TST_AC05 (94 percent), were at level E. However, of the 63 records of TSR test data at level E, 33 did not have layer and material description information and were not used in further analysis. A feedback report was sent to the FHWA to determine material type. These test results will be added to the representative data table when the status of the material type information is made available.

Table 37. Level 1 completeness for table TST_AC05.

Expt. Type Expt. Number Total Number of Records at All Levels Total Number of Records at Level E Percentage of Records at Level E Number of Analysis Cells Represented at Level E

GPS 6S 2 -- -- --

SPS-1 1 57 33 57.9 13

SPS-5 5 32 21 65.6 7

SPS-8 8 22 9 40.9 3

Expt. Type	Expt. Number	Total Number of Records at All Levels	Total Number of Records at Level E	Percentage of Records at Level E	Number of Analysis Cells Represented at Level E
GPS	6S	2	--	--	--
SPS-1	1	57	33	57.9	13
SPS-5	5	32	21	65.6	7
SPS-8	8	22	9	40.9	3

A total of 20 records were from SPS supplementary sections. Test data from SPS supplemental sections were removed from further analysis.

Level 2--Data Completeness

For level 2 data completeness, the analysis cells were further evaluated for completeness. Checking for completeness involved the following:

Determine the minimum number of test results required for each analysis cell.
Determine the actual number of test results at level E for each analysis cell.
Compare the actual and required number of test results for each analysis cell.

Analysis cells with less than the required number of test data were classified as incomplete, whereas those with at least the minimum number of test were classified as complete. Approximately 56 percent of the analysis cells evaluated were complete. Table 38 presents a detailed summary of the results of the level 2 data completeness analysis.

Table 38. Summary of level 2 data completeness assessment for table TST_AC05.

Expt. Type	No. of Analysis Cells with Test Data and Material Information	Min. Number of Test Results Required	Analysis Cells with Minimum Number of Test Results	Percent Analysis Cells with Minimum Test Results
SPS-1	6	3	4	67
SPS-5	3	6	0	0
SPS-8	2	3	2	100

Quality Assessment of Moisture Susceptibility of Asphalt Concrete Data

Data quality was evaluated by checking for compliance with test protocols, by checking the reasonableness of each individual test result, and by checking the level of variability in each analysis cell. Descriptions of the data quality checks and results are presented in the next few sections.

Compliance with Test Protocols

The test data were evaluated for compliance with testing protocols. SHRP P05 and AASHTO T283 recommend that all test specimens should have the following properties:

Percent air voids content--Test specimens should be compacted to an air voids content between 6 to 8 percent or the air void content expected in the field (3 to 5 percent).
Specimen thickness--Recommended specimen thickness is 63.5 mm.
Specimen diameter--Recommended specimen diameter is 102 mm.

The specimens used in testing were evaluated to check for compliance with the SHRP and AASHTO recommendations.

Percent Air Voids

Most of the test specimens had air void contents within either the target range of 3 to 5 percent or the range proposed by AASHTO T283 (6 to 8 percent). They were, therefore, in compliance with the testing protocol.

Specimen Thickness

The specimens used for testing generally were in agreement with the SHRP and AASHTO recommendations, with the exception of specimens from site 490800. Specimen thickness from this site ranged from 68.8 to 71.4 mm, which is much higher than the recommended 63.5 mm. The results were checked further to determine their effect on test results. The average specimen thicknesses for the remaining specimens were 65 mm, which is reasonable.

Specimen Diameter

SHRP P05 recommends a specimen diameter of 102 mm. With the exception of the specimens from site 490800 and 530800, which had diameters of 101.6 mm, all remaining specimen diameters were 102 mm. A specimen diameter of 101.6 mm was found not to significantly affect test results.

Data Reasonableness

The test data were evaluated for reasonableness. Theorectically, TSR could range from 0 to 1.0. However, most test data typically fall between 0.35 and 0.95. For the test results in table TST_AC05, TSR ranged from 0.49 to 1.05.⁽²³⁾

Test results from test section 490800 (SPS-8) had a TSR of 1.05, which seems unreasonable, though statistically possible. A review of the test records for these results shows that the specimens had severe stripping and loss of fine and coarse aggregate.

The remaining test results were reasonable, showing no obvious anomaly. The distribution of TSR test values in table TST_AC05 is presented in figure 33. Approximately 70 percent of the TSR values fall within a range of 0.7 to 0.9.

Figure 33. Distribution of the TSR in table TST_AC05.

Figure 33. Distribution of Tensile Strength Ratio (TSR) in table TEST_AC05. The bar graph shows TSR on the horizontal axis, Number of Tests on the left vertical axis, and Cumulative Percentage on the right vertical axis. N = 30, mean = 0.82, stdev = 0.10, and range = 0.62-1.09.

Missing Values

Some of the test records for test section 530800 were missing. Feedback reports³ were sent to the FHWA reporting the anomaly. The FHWA will implement the appropriate remedial actions and upgrade the relevant test tables accordingly.

³Feedback reports enable users of the LTPP database to report any situations encountered during FHWA-sponsored data analysis that suggest or demonstrate the need for corrective actions or further investigation. Such situations include, but are not limited to:

The absence of critical data for specific test sections.
Data that appear to be incorrect, contradictory, or otherwise suspect.
Data that are not currently collected but are needed to fill voids identified during analysis.
Recommendations arising from the analysis of how data collection procedures might be improved.

Assessing TSR Data Quality

The first step in assessing TSR data quality was to determine typical variability expected for test results from cores belonging to the same analysis cell. This was done by reviewing typical variability measured as standard deviation or COV in published literature.⁽²³⁾ A summary of typical variability in measured tensile strength is as follows:⁽²³⁾

Tensile strength--350 to 1,900 kPa.
TSR--0.35 to 0.85.
Standard deviation--15 to 80 kPa.
COV--1.9 to 11.2 percent.

A COV of 10 percent was, therefore, adopted and used in data evaluation. The TSR data in table TST_AC05 had only one analysis cell with COV greater than 10 percent. The distribution of COV for analysis cells with TSR test results is presented in figure 34.

Figure 34. Distribution of COV for TSR.

Figure 34. Distribution of COV for TSR. The bar graph shows COV in percent on the horizontal axis, Number of Analysis Cells on the left vertical axis, and Cumulative Percentage on the right vertical axis. Analysis Cells with a COV of less than 10% are considered Acceptable and those greater than 10% are Questionable. About 90% of the Analysis Cells are Acceptable.

Identification of Anomalous Data

The anomalies found in the data during the various analyses performed are described below. Where possible, corrective or remedial measures were implemented to address the identified anomalies.

Anomaly 1: Noncompliance with Test Protocols

Some of the test results were obtained using testing practices (e.g., specimen preparation and dimension) that did not comply with the test protocol. Test results obtained using non-compliant procedures were not used in the development of the representative data tables.

Anomaly 2: Excessive Variability

A single analysis cell (1 of 10) had excessive variability (COV > 10 percent) and was classified as questionable. Test data that fall outside the 4 standard deviation of the mean were classified as erroneous and not used in developing the representative data table and summary statistics. This was duly noted with an appropriate comment code in the representative test table.

Schema of the Representative Moisture Susceptibility Data Table TST_AC05_REP

The representative moisture susceptibility data table TST_AC05_REP was developed and recommended for inclusion into the LTPP database. The schema for the representative data table is as follows:

State code.
SHRP identification number.
SPS cell identification number.
Layer description.
Material code.
Construction number.
Number of TSR tests.
Mean TSR.
Maximum TSR.
Minimum TSR.
COV of TSR.
Standard deviation of TSR.
QA_Comment_1.
QA_Comment_2.
QA_Comment_3.
QA_Comment_4.
QA_Comment_5.
QA_Comment_6.
QA_ Comment_Other.
Record status.

Moisture susceptibility testing is conducted only for SPS experiments . The first six fields in the schema define the analysis cell used in computing summary statistics of the measured TSR values. The representative test results and basic statistics are presented in fields 7 to 12, followed by comment fields that describe the quality status of the data (e.g., excessive variability or incomplete sampling) and the record status of the data.

Recommendations

It is recommended that additional information be added to the data in table TST_AC05 to describe the condition (e.g., stripping and raveling) of AC cores from both GPS and SPS experiments. This will provide additional information as to whether the AC mixture survived under prevailing pavement conditions. The age of the specimen at the time of inspection is also very relevant to place the data on the state of the AC core in its proper context. Finally, information on the type and amount of antistripping agents applied should be provided, where relevant.

9. VISUAL EXAMINATION AND LENGTH MEASUREMENT OF PCC CORES

Introduction

Visual examination and length measurement are performed on PCC core specimens as part of the material characterization program for the LTPP study. These basic tests are conducted on all PCC cores before they are subjected to other tests (e.g., compressive strength, split tensile strength, and static modulus of elasticity testing).

Cores are usually taken at specified periods (e.g., 14, 28, and 365 days) after construction for newly constructed pavements and several years after construction for in-service pavements adopted into the LTPP program. The number of cores taken from a given location depends on both the pavement test section properties and LTPP experiment type.

Procedures used for measuring core length (layer thickness) and examining the cores are presented in the following SHRP test protocol and AASHTO/ASTM test procedures:

SHRP P66--Visual examination and length measurement of PCC cores.⁽²⁾
AASHTO T148--Measuring length of drilled concrete cores.⁽⁴⁾
ASTM C856--Petrographic examination of hardened concrete.⁽³⁾

The relevant GPS and SPS materials test and data collection guides provide guidance on all aspects of material sampling and testing (See references 7, 17 through 20, and 23.) These guidelines include:

Core specimen sampling.
Sample preparation.
Laboratory testing.
Computation of test results.
Presentation of test results.

The PCC core specimens are examined visually to determine their general condition, presence of distresses, presence of defects such as cracks, voids, D-cracking, alkali-silica reactivity, and problems with layer separation (for overlaid pavements).^(2,3,4) The general type and shape of aggregates (e.g., rounded gravel or angular crushed stone) were also documented. The cores were also examined to determine their suitability for length measurements and other testing. Cores with serious defects, such as uneven surfaces and segregated aggregates, were noted and not used for further testing.^{(2, 3, 4)}

Visual examination does not include detailed core examination, such as petrographic and stereo microscopic examinations. Results of visual examination are reported using standard SHRP codes as described in SHRP protocol P66 attachment A.⁽²⁾

The length measurement and visual examination test results are stored in the LTPP database after undergoing QC checks that ensure anomalies (e.g., negative PCC core thickness values) are identified and corrected. Data that are of acceptable quality after the QC checks are classified as level E and are stored in table TST_PC06. The following information is maintained in table TST_PC06:

SHRP identification number.
State code.
Layer number.
Field set.
Test number.
Location number.
Construction number.
Laboratory code.
Sample area number.
Core average thickness.
Visual examination 1.
Visual examination 2.
Visual examination 3.
Visual examination 4.
Visual examination 5.
Visual examination 6.
Visual examination other.
Comments (six fields).
Comments other.
Test date.
Sample number.
Record status.

Material Sampling

Core specimens are collected as directed by the SHRP P66 protocol from designated locations within the GPS and SPS experiments (GPS experiments are cored by SHRP contractors; SPS experiments are cored by SHA's or contractors). Cores are then prepared according to procedures outlined in SHRP P66, AASHTO T148, and ASTM C856 before being shipped to certified laboratories for testing.

For GPS pavements, cores are taken from both the approach and leave ends of the pavement test section. Both core locations are sited some distance from the monitored test section to avoid damaging the test section. For SPS experiments, cores are taken at designated locations from various test sections within a given site. Full details of the sampling plans, material preparation, and testing are presented in the relevant SHRP protocols, AASHTO test procedures, and the Data Collection Guidelines for SPS Experiments. (See references 17 through 23.)

For SPS experiments, sampling can be complicated because these experiments consist of a diverse matrix of test sections, some of which are newly constructed with different PCC target strengths (e.g., SPS-2), and the others are in-service pavements that have been overlaid and, therefore, consist of both in-service and newly constructed (e.g., SPS-7). For SPS-2 experiments, cores are taken from the different test sections to obtain samples representing the different target strength levels. SPS-7 pavements are cored pre- and postoverlay to obtain cores for both layers. Sampling was, therefore, experiment-specific, and the minimum number of core specimens required for testing differed for the GPS and SPS experiments.

PCC Core Thickness Data Completeness

The first step in assessing data completeness was the extraction and assembly of the data available in table TST_PC06 in the LTPP database (January 2000 release); this process was described in detail in the introductory chapters of this report.

Level 1--Visual Examination and Thickness Data Completeness

A summary of the visual examination and thickness data collected for all GPS and SPS experiments at all record status levels (A to E) is presented in table 39. SPS experiments 2, 6, 7, and 9 have sublevel E data, which are outside the scope of this study and will not be considered in the analysis. Further, not all the records available at level E were usable because the data either pertained to supplemental SPS experiments, did not contain material type information from the corresponding TST_L05B database table, had missing core thickness values, or had been wrongly included in the database (ascertained based on the material code from TST_L05B table). The extent of data in each of these categories is also noted in table 39. Recall that supplemental sections are also outside the scope of this study. TST_L05B material type information is important to identify analysis cells, as well as to verify whether the data belong in the table. Without this information, data analysis cannot be carried out with confidence.

Figures 35 and 36 present total data availability for GPS and SPS pavement test sections, along with the amount of usable data at level E. In all, 657 of 4,897 records were excluded from further analysis for reasons such as missing thickness information or belonging to SPS supplemental sections (that fall out of the scope of this study).

Table 39. Summary of average core thickness data available in table TST_PC06.

Expt. Type Expt. No. No. of Records at Levels A to E No. of Records at Level E No. of Usable Records at Level E Percent Usable Records at Level E Total Number of Analysis Cells Represented

GPS 3 824 818 818¹ 99 123

GPS 4 440 436 436² 99 61

GPS 5 545 540 540³ 99 81

GPS 7A 330 330 324⁴ 98 35

GPS 7B 67 67 60⁵ 90 7

GPS 9 331 331 324⁶ 98 48

SPS 1 26 0 0 0 --

SPS 2 1354 1121 990⁷ 73 116--PCC
36--LCB

SPS 6 231 168 168 73 51

SPS 7 552 536 502⁸ 91 25--Original
28--Overlay

SPS 8 16 16 16 100 2

SPS 9 170 82 30⁹ 18 2

Expt. Type	Expt. No.	No. of Records at Levels A to E	No. of Records at Level E	No. of Usable Records at Level E	Percent Usable Records at Level E	Total Number of Analysis Cells Represented
GPS	3	824	818	818¹	99	123
GPS	4	440	436	436²	99	61
GPS	5	545	540	540³	99	81
GPS	7A	330	330	324⁴	98	35
GPS	7B	67	67	60⁵	90	7
GPS	9	331	331	324⁶	98	48
SPS	1	26	0	0	0	--
SPS	2	1354	1121	990⁷	73	116--PCC 36--LCB
SPS	6	231	168	168	73	51
SPS	7	552	536	502⁸	91	25--Original 28--Overlay
SPS	8	16	16	16	100	2
SPS	9	170	82	30⁹	18	2

¹Six records do not have corresponding L05B information.
²Four records are missing thickness information.
³Five records are missing thickness information.
⁴Six records are missing thickness information.
⁵Two records are missing thickness information; five records have discrepancies in material type descriptions.
⁶One record is missing thickness information; six records do not have corresponding L05B information.
⁷Eighty-eight records do not have corresponding L05B information; 41 records are missing thickness information; 2 records have discrepancies in material type descriptions.
⁸Eleven records do not have corresponding L05B information; 23 records are missing thickness information.
⁹Fifty-two records pertaining to supplemental sections are present at level E.

Definition of Analysis Cells

Table 39 presents a summary of the analysis cells represented by the level E data. For the thickness test data, an analysis cell was defined as a layer of a given material and construction type within a test section for which data were available. Analysis cells are extremely useful in the level 2 data completeness analysis, as well as in data quality evaluation.

Figure 35. Histogram of PCC core specimen data availability for GPS pavement sections.

Figure 36. Histogram of PCC core specimen data availability for SPS pavement sections.

Table 40. Analysis cell definitions for test table TST_PC06.

Key Fields GPS Experiments (All) SPS Experiments (All)

Experiment type X X

Experiment number X X

SHRP_ID X X

State code X X

Construction number X X

Layer number X X

Material code X X

Key Fields	GPS Experiments (All)	SPS Experiments (All)
Experiment type	X	X
Experiment number	X	X
SHRP_ID	X	X
State code	X	X
Construction number	X	X
Layer number	X	X
Material code	X	X

Table 40 presents a list of fields that make up analysis cells for both GPS and SPS experiments for test table TST_PC06. Note that the SHRP_ID field was part of the analysis cell definition for SPS experiments. This was because LTPP sampling for PCC thickness measured ensured that adequate amounts of specimens were obtained from individual test sections (within the SPS experiments) for testing (see table 41). This was unlike testing for other data elements, where sampling was done from selected test sections and averaged to represent the whole experiment.

Level 2--Thickness and Visual Examination Data Completeness

Level 2 data completeness consisted of checking each analysis cell to determine whether the required minimum number of samples were collected and tested, as per the material sampling and testing requirements.

As a first step in checking level 2 data completeness, the number of specimens tested and reported in table TST_PC06 for each analysis cell was computed. The computed number of tests per cell was then compared with the minimum number of tests required by LTPP.

Table 41 presents a summary of the minimum core specimens required by the LTPP materials testing and sampling program for core length measurements and visual examination for each LTPP experiment. Although the minimum number of tests per layer indicated in the table directly translates to the required minimum tests per analysis cell for GPS projects, the same is not true for SPS projects. Therefore, a more detailed description of the testing requirements for the various SPS experiments is presented in table 42.

Analysis cells with the minimum number of test results were categorized as complete, whereas those with less than the minimum were classified as incomplete. Table 43 presents a summary of level 2 data completeness for all the analysis cells in table TST_PC06. Table 43 also indicates the number of analysis cells with single test values. Procedures employed to analyze the reasonableness and validity of the data contained in cells with single test values differ from those with information from multiple tests because characterizing variability is not possible for these cells.

Table 41. Summary of the sampling and testing plan for thickness measurement and visual examination of PCC cores.

Experiment Type Layer Type Test Type LTPP Designation LTPP Protocol Minimum Number of Tests per Layer Sampling Location¹

GPS-3, 4, 5, & 9 PCC Core examination and thickness PC06 P66-61 2 C2, C8

GPS-7 PCC Core examination and thickness PC06 P66-61 2 C8, C20

GPS-3, 4, 5 & 9 PCC Core examination and thickness PC06 P66-62 2 C5, C11

GPS-7 PCC Core examination and thickness PC06 P66-62 2 C11, C23

GPS-3, 4, 5 & 9 PCC Core examination and thickness PC06 P66-64 2 C1, C7

GPS-7 PCC Core examination and thickness PC06 P66-64 2 C7, C19

SPS-2 PCC Core examination and thickness PC06 P66 99 per site/project All cores

SPS-2 LCB Core examination and thickness PC06 P66 24 per site/project --

SPS-6 PCC Core examination and thickness PC06 P66 23 per site/project C1-C20 A1 A2 A3

SPS-7 PCC--overlay (postconstruction) Core examination and thickness PC06 P66 99 per site/project C10-20 C21-64 C65-108

SPS-7 PCC--overlay (preconstruction) Core examination and thickness PC06 P66 47 per site/porject C10-20 C21-64 C65-108

SPS-8 PCC Core examination and thickness PC06 P66 26 per site/project C1-C26

SPS-9A PCC Core examination and thickness PC06 P66 6 per site/project --

Experiment Type	Layer Type	Test Type	LTPP Designation	LTPP Protocol	Minimum Number of Tests per Layer	Sampling Location¹
GPS-3, 4, 5, & 9	PCC	Core examination and thickness	PC06	P66-61	2	C2, C8
GPS-7	PCC	Core examination and thickness	PC06	P66-61	2	C8, C20
GPS-3, 4, 5 & 9	PCC	Core examination and thickness	PC06	P66-62	2	C5, C11
GPS-7	PCC	Core examination and thickness	PC06	P66-62	2	C11, C23
GPS-3, 4, 5 & 9	PCC	Core examination and thickness	PC06	P66-64	2	C1, C7
GPS-7	PCC	Core examination and thickness	PC06	P66-64	2	C7, C19
SPS-2	PCC	Core examination and thickness	PC06	P66	99 per site/project	All cores
SPS-2	LCB	Core examination and thickness	PC06	P66	24 per site/project	--
SPS-6	PCC	Core examination and thickness	PC06	P66	23 per site/project	C1-C20 A1 A2 A3
SPS-7	PCC--overlay (postconstruction)	Core examination and thickness	PC06	P66	99 per site/project	C10-20 C21-64 C65-108
SPS-7	PCC--overlay (preconstruction)	Core examination and thickness	PC06	P66	47 per site/porject	C10-20 C21-64 C65-108
SPS-8	PCC	Core examination and thickness	PC06	P66	26 per site/project	C1-C26
SPS-9A	PCC	Core examination and thickness	PC06	P66	6 per site/project	--

¹Details of the core locations are presented in the respective LTPP materials sampling guides.

Table 42. Details of sampling for core visual examination and length measurement for SPS experiments.

Experiment Type Test Section Layer Type Min. No. of Specimens Required per Layer Sampling Location¹

SPS-2 201 (215) PCC 8 C10-C17

SPS-2 202 (216) PCC 8 C59-C66

SPS-2 203 (213) PCC 9 C34-C42

SPS-2 204 (214) PCC 8 C83-C90

SPS-2 205 (219) PCC 8 C21-C25C18-C20, C22-C24

SPS-2 206 (220) PCC 8 C73-C74C67-C72

SPS-2 207 (217) PCC 8 C29-C33C26-C28, C30-C32

SPS-2 208 (218) PCC 8 C78, C82C75-C77, C79-C81

SPS-2 209 (223) PCC 9 C1-C9

SPS-2 210 (224) PCC 8 C51-C58

SPS-2 211 (221) PCC 8 C43-C50

SPS-2 212 (222) PCC 9 C91-C99

SPS-2 205 (219) LCB 6 C18-C20, C22-C24

SPS-2 206 (220) LCB 6 C67-C72

SPS-2 207 (217) LCB 6 C26-C28, C30-C32

SPS-2 208 (218) LCB 6 C75-C77, C79-C81

SPS-6 601 Original PCC 3 C1-C3

SPS-6 602 Original PCC 4 C4-C6, A1

SPS-6 603 Original PCC 3 C11-C12

SPS-6 604 Original PCC 2 C13-C14

SPS-6 605 Original PCC 4 C7-C10

SPS-6 606 Original PCC 2 C15-C16

SPS-6 607 Original PCC 3 C17-C18, A3

SPS-6 608 Original PCC 2 C19-C20

SPS-7 701 Original (Overlay) 1 (0) A1

SPS-7 702 Original (Overlay) 1 (8) A2 (C21-C24, C65-C68)

SPS-7 703 Original (Overlay) 3 (9) C1-C3 (C10, C25-C28, C69-C72)

SPS-7 704 Original (Overlay) 1 (8) A3 (C29-C32, C73-C76)

SPS-7 705 Original (Overlay) 4 (8) A4, BA1-BA3 (C33-C36, C77-C80)

SPS-7 706 Original (Overlay) 3 (11) C4-C6 (C11-C13, C37-C40, C81-C84)

SPS-7 707 Original (Overlay) 1 (17) A5 (C14-C16, C44-C50, C88-C94)

SPS-7 708 Original (Overlay) 1 (16) A6 (C17-C18, C51-C57, C95-C101)

SPS-7 709 Original (Overlay) 3 (16) C7-C9 (C19-C20, C58-C64, C102-C108)

SPS-8 807, 809, 811 Overlay 13 C1-C13

SPS-8 808, 810, 812 Overlay 13 C14-C26

SPS-9A 901, 902, 903 Original 2 --

Experiment Type	Test Section	Layer Type	Min. No. of Specimens Required per Layer	Sampling Location¹
SPS-2	201 (215)	PCC	8	C10-C17
SPS-2	202 (216)	PCC	8	C59-C66
SPS-2	203 (213)	PCC	9	C34-C42
SPS-2	204 (214)	PCC	8	C83-C90
SPS-2	205 (219)	PCC	8	C21-C25C18-C20, C22-C24
SPS-2	206 (220)	PCC	8	C73-C74C67-C72
SPS-2	207 (217)	PCC	8	C29-C33C26-C28, C30-C32
SPS-2	208 (218)	PCC	8	C78, C82C75-C77, C79-C81
SPS-2	209 (223)	PCC	9	C1-C9
SPS-2	210 (224)	PCC	8	C51-C58
SPS-2	211 (221)	PCC	8	C43-C50
SPS-2	212 (222)	PCC	9	C91-C99
SPS-2	205 (219)	LCB	6	C18-C20, C22-C24
SPS-2	206 (220)	LCB	6	C67-C72
SPS-2	207 (217)	LCB	6	C26-C28, C30-C32
SPS-2	208 (218)	LCB	6	C75-C77, C79-C81
SPS-6	601	Original PCC	3	C1-C3
SPS-6	602	Original PCC	4	C4-C6, A1
SPS-6	603	Original PCC	3	C11-C12
SPS-6	604	Original PCC	2	C13-C14
SPS-6	605	Original PCC	4	C7-C10
SPS-6	606	Original PCC	2	C15-C16
SPS-6	607	Original PCC	3	C17-C18, A3
SPS-6	608	Original PCC	2	C19-C20
SPS-7	701	Original (Overlay)	1 (0)	A1
SPS-7	702	Original (Overlay)	1 (8)	A2 (C21-C24, C65-C68)
SPS-7	703	Original (Overlay)	3 (9)	C1-C3 (C10, C25-C28, C69-C72)
SPS-7	704	Original (Overlay)	1 (8)	A3 (C29-C32, C73-C76)
SPS-7	705	Original (Overlay)	4 (8)	A4, BA1-BA3 (C33-C36, C77-C80)
SPS-7	706	Original (Overlay)	3 (11)	C4-C6 (C11-C13, C37-C40, C81-C84)
SPS-7	707	Original (Overlay)	1 (17)	A5 (C14-C16, C44-C50, C88-C94)
SPS-7	708	Original (Overlay)	1 (16)	A6 (C17-C18, C51-C57, C95-C101)
SPS-7	709	Original (Overlay)	3 (16)	C7-C9 (C19-C20, C58-C64, C102-C108)
SPS-8	807, 809, 811	Overlay	13	C1-C13
SPS-8	808, 810, 812	Overlay	13	C14-C26
SPS-9A	901, 902, 903	Original	2	--

¹Details of the core locations are presented in the respective LTPP materials sampling guide.

Table 43. Summary of core thickness data available for GPS and SPS experiments.

Experiment Type Layer Type Total Number of Cells Number of Cells with Less Than Minimum Number of Tests Percent Cells with Incomplete Data

GPS-3 PCC original 123 0 0

GPS-4 PCC original 61 0 0

GPS-5 PCC original 81 0 0

GPS-7A PCC original 35 0 0

GPS-7B PCC original 7 1
(1 single test value) 14

GPS-9 PCC original 48 0 0

SPS-2 PCC 116 60
(1 single test value) 52

SPS-2 LCB 36 12
(1 single test value) 33

SPS-6 PCC 51 12
(5 single test values) 24

SPS-7 PCC original 25 5
(2 single test values) 20

SPS-7 PCC overlay 28 28
(1 single test value) 100

SPS-8 PCC 2 2 100

SPS-9A PCC 6 0 0

Experiment Type	Layer Type	Total Number of Cells	Number of Cells with Less Than Minimum Number of Tests	Percent Cells with Incomplete Data
GPS-3	PCC original	123	0	0
GPS-4	PCC original	61	0	0
GPS-5	PCC original	81	0	0
GPS-7A	PCC original	35	0	0
GPS-7B	PCC original	7	1 (1 single test value)	14
GPS-9	PCC original	48	0	0
SPS-2	PCC	116	60 (1 single test value)	52
SPS-2	LCB	36	12 (1 single test value)	33
SPS-6	PCC	51	12 (5 single test values)	24
SPS-7	PCC original	25	5 (2 single test values)	20
SPS-7	PCC overlay	28	28 (1 single test value)	100
SPS-8	PCC	2	2	100
SPS-9A	PCC	6	0	0

Thickness and Visual Examination Data Quality

The quality of the thickness data was determined by evaluating the data for reasonableness of test values, compliance with test protocols, and variability in test values (for analysis cells with multiple specimens).

For quality evaluation of visual examination data, results from each core specimen were reviewed to determine the condition of the specimen and possible conflicts in visual examination results. Specimens with discrepancies were identified. This evaluation is important in identifying the source of anomalies, not only for the thickness data under investigation in this chapter, but for all other data elements obtained through testing the PCC cores after visual examination and thickness measurement testing. A summary of results from the data quality evaluation is presented in the following sections.

Reasonableness of Core Thickness Values

Evaluating whether PCC core thickness measurements are reasonable involved examining the core thickness values for each layer to detect obvious errors and anomalies, such as negative values and values close to zero. Analysis cells that were termed incomplete due to inadequate testing were also evaluated for bias in the core thickness values. The thickness data from all 4,208 core specimens evaluated did not contain any obvious anomalies. Table 44 presents basic statistics of the core thickness data available in table TST_PC06.

Table 44. Summaries of descriptive statistic for core thickness data in table TST_PC06.

Experiment Type Target Thickness, mm Number of Specimens Mean Thickness, mm Standard Deviation, mm¹ COV, percent¹ Min Thickness Range, mm Max Thickness Range, mm

GPS-3 -- 818 240.9 31.6 13.1 152.4 370.8

GPS-4 -- 436 240.2 20.9 8.7 190.5 302.3

GPS-5 -- 540 221.8 27.5 12.4 154.9 335.3

GPS-7A -- 324 215.6 22.4 10.4 165.1 261.6

GPS-7B -- 60 225.1 23.7 10.5 190.5 271.8

GPS-9 -- 324 218.6 25.2 11.5 165.1 335.3

SPS-2 (203-mm sections) 203.2 414 211.5 17.3 8.2 127.0 287.0

SPS-2 (279-mm sections) 279.4 390 277.8 28.5 10.3 129.0 325.1

SPS-2 (sections with LCB) 152.4 186 162.4 14.8 9.1 120.9 213.4

SPS-6 -- 168 232.4 17.1 7.4 198.1 266.7

SPS-7 (original PCC layer) -- 203 192.5 29.9 15.6 53.3 218.4

SPS-7 (75-mm overlay sections) 76.2 114 164.4 91.3 55.5 74.7 307.3

SPS-7 (125-mm overlay sections) 127.0 185 166.8 64.8 38.8 101.6 353.1

SPS-8 (200-mm sections) 203.2 9 193.0 6.7 3.5 183.4 200.7

SPS-8 (275-mm sections) 279.4 7 283.1 5.8 2.1 278.4 294.1

SPS-9 -- 30 227.4 13.6 6.0 203.2 246.4

Experiment Type	Target Thickness, mm	Number of Specimens	Mean Thickness, mm	Standard Deviation, mm¹	COV, percent¹	Min Thickness Range, mm	Max Thickness Range, mm
GPS-3	--	818	240.9	31.6	13.1	152.4	370.8
GPS-4	--	436	240.2	20.9	8.7	190.5	302.3
GPS-5	--	540	221.8	27.5	12.4	154.9	335.3
GPS-7A	--	324	215.6	22.4	10.4	165.1	261.6
GPS-7B	--	60	225.1	23.7	10.5	190.5	271.8
GPS-9	--	324	218.6	25.2	11.5	165.1	335.3
SPS-2 (203-mm sections)	203.2	414	211.5	17.3	8.2	127.0	287.0
SPS-2 (279-mm sections)	279.4	390	277.8	28.5	10.3	129.0	325.1
SPS-2 (sections with LCB)	152.4	186	162.4	14.8	9.1	120.9	213.4
SPS-6	--	168	232.4	17.1	7.4	198.1	266.7
SPS-7 (original PCC layer)	--	203	192.5	29.9	15.6	53.3	218.4
SPS-7 (75-mm overlay sections)	76.2	114	164.4	91.3	55.5	74.7	307.3
SPS-7 (125-mm overlay sections)	127.0	185	166.8	64.8	38.8	101.6	353.1
SPS-8 (200-mm sections)	203.2	9	193.0	6.7	3.5	183.4	200.7
SPS-8 (275-mm sections)	279.4	7	283.1	5.8	2.1	278.4	294.1
SPS-9	--	30	227.4	13.6	6.0	203.2	246.4

¹Standard deviation and COV values reported are for all samples within a given experiment.

The data were broadly classified by experiment type, but further subdivisions based on layer type and target strength were made, where applicable, for clarity and for assessing whether the data were reasonable.

For data from GPS experiments, conclusions regarding the reasonableness of data can be drawn by considering the range of core thicknesses within each experiment. The PCC core thicknesses for the GPS, SPS-6, and SPS-9 projects (all original pavements) ranged from 152 mm to 371 mm. The range of values observed seem to be in line with normal range of PCC thicknesses commonly encountered in practice. The standard deviations and COV's noted in the table for GPS 1 through 9, SPS-6, and SPS-9 experiments are just an indication of the spread of PCC layer thicknesses across all projects within each experiment. These values should not be confused with the variability in thickness data associated with multiple specimens from a given analysis cell, which is discussed later in this chapter.

The range of core thicknesses of the SPS-7 original pavement sections was between 53 mm and 218 mm. Closer examination of the data revealed that there were 19 records with thicknesses less than 125 mm. All these cores have been commented as "broken short cores," which explains the unusually low PCC layer thicknesses noted in the range. The rest of the cores range in thickness from 125 mm to 218 mm, which is well within the normal range of concrete slab thicknesses encountered in practice.

A more detailed evaluation of SPS-2 and SPS-8 thickness data was possible because these experiments identify each section with a specific target thickness. Although the focus of this study was not to identify pavement test sections that do not achieve target thickness, excessive variation from set targets is an indication of poor quality control and the potential for excessive variability in test results from PCC layers within those pavement sections.

Based on the information presented in table 44, the SPS-2 and SPS-8 experiments, in general, had thicknesses close to the target layer thickness. The difference between measured mean core thickness and target thickness for these experiments ranged from 2 to 10 mm. However, for the SPS-7 experiment overlay sections, there was a vast difference in target and measured thickness for PCC overlays. The difference was over 100 percent for the 75-mm overlay sections and approximately 31 percent for the 125-mm overlay sections. This level of variability is excessive and needs further investigation.

Figures 37 and 38 show plots of the distribution of core thicknesses for 75-mm overlay and 125-mm overlay sections, respectively. For the overlay sections with a target thickness of 75 mm, all but one of the cores had a thickness greater than the target value. More importantly, approximately 64 percent of the core specimens were between 100 and 150 mm thick. Most of the remaining specimens have thicknesses way beyond the target value and are concentrated between 275 and 325 mm. For the 125-mm overlay sections, 87 percent of the cores have core thicknesses ranging from 100 to 175 mm. This shows that a majority of the data is within reasonable proximity of the target value. However, as with the 75-mm thick overlay sections, a majority of the remaining cores have thicknesses far in excess of the target value and are concentrated in the range of 300 to 375 mm. Further analysis revealed that all the sections showing large deviations between the specified and as-constructed values were concentrated in one particular State. It must be noted here that some State highway agencies, in pursuance of their local policies, may have adopted overlay thicknesses that were different from specified values.

Figure 37. Plots of distribution of core thickness for SPS-7 75-mm overlay sections.

Figure 37. Plots of distribution of core thickness for SPS-7 75-millimeter overlay sections. The bar graph shows Core Thickness in millimeters on the horizontal axis and Number of Cores on the vertical axis. For Thicknesses of 75, 100, 125, 275, 300, and 325 millimeters there are about 2, 43, 32, 7, 30, and 3 Cores, respectively.

Figure 38. Plots of distribution of core thickness for SPS-7 125-mm overlay sections.

Figure 38. Plots of distribution of core thickness for SPS-7 125-millimeter overlay sections. The bar graph shows Core Thickness in millimeters on the horizontal axis and Number of Cores on the vertical axis. For Thicknesses of 125, 150, 175, 200, 325, 350, and 375 millimeters there are about 18, 98, 43, 1, 5, 16, 2 Cores, respectively.

In summary, all the thickness values presented in table 44 appear to be reasonable. Even the core thicknesses from the SPS-7 sections are within reasonable ranges (when the thickness data from test sections with thicknesses greater than the target values are factored out). Further, even the data from the test sections with PCC thickness greater than the target thickness values were consistent with each other.

Evaluation for Noncompliance with Testing and Sampling Procedures

The next stage in assessing data quality was to identify pavement test sections with possible bias in results due to inadequate sampling or deviations from test procedures. The SHRP P66 test protocol and AASHTO T148 had no recommendation for assessing bias due to testing, and there were no obvious deviations from recommended testing procedure. Bias assessment was, therefore, limited to the component contributed by incomplete sampling and testing. Pavement test sections sampled according to the relevant portions of SHRP P66 and AASHTO T148 should experience no sampling bias; however, for pavements with less than the required number of tests performed, the test results may not be representative of the whole pavement test section.

Test pavements with less than the minimum number of core specimens tested were classified as incomplete (has a high potential for bias), whereas those with the minimum cores tested were classified as complete (no sampling bias). Test completeness for all the PCC and SPS-2 LCB analysis cells was previously summarized in table 43.

It can be inferred from the table that the GPS cells, with the exception of one cell in the GPS-7B experiment, had more than the required number of core specimens tested and, therefore, had no sampling bias. The GPS-7B cell has a single test value. This was treated as an anomaly, and appropriate remedial actions were adopted to rectify this anomaly, where possible.

In contrast, most of the SPS experiments had a significant amount of incomplete cells. The most affected was the SPS-7 experiment, which, incidentally, has a very high number of tests required per test section (see table 42). An examination of these analysis cells showed that most contained more than four test results from cores drawn from various locations within the test section. A high number of test results (though less than the required number of test results) reduces the likelihood of bias in representative test values. Further, it was discovered that a substantial amount of SPS-2 and SPS-6 data are still undergoing QA/QC checks. It is, therefore, likely that any apparent bias in representative test values due to incomplete testing is transitory and that more material testing data will available at level E in the near future.

Thickness Data Variability

Pavement test sections with multiple thickness data for PCC and LCB layers were evaluated to determine the level of variability in test results within the test sections. Analysis cells with single test values (see table 43) could not be considered in the analysis, for obvious reasons. Test data for the sections with multiple specimen thickness measurements were classified as acceptable or questionable, based on the level of variability estimated. The analysis was performed separately for layers with complete and incomplete testing to investigate whether the core thickness variability in these two categories differed. Any differences can be attributed to bias introduced by inadequate testing. The AASHTO T148 (ASTM C174) standard for measuring length of drilled concrete cores has no statement on the precision. However, several pavement construction and reliability studies have published typical variability expected for PCC layer thickness along a highway. Table 45 presents a summary of the range of typical COV's and standard deviations for thickness of PCC layers published in various literature.

Based on the data presented in table 45, a standard deviation of 8 mm was adopted and used for categorizing acceptable and questionable PCC layer thickness data. The same cutoff value (8 mm) was adopted for evaluating LCB layer thickness data (from SPS-2 experiments) included in table TST_PC06. This assumption was reasonable because the same procedures and protocols used in placing, testing, and evaluating PCC layers were used for LCB layers.

Thickness Data Quality Classification

Mean, standard deviation, and COV of the PCC and LCB thickness were computed for analysis cells with multiple test data. For GPS experiments, all specimens cored for a given pavement section were used in determining the statistics. For SPS experiments, the statistics were calculated from cores taken from each individual test section (e.g., 201, 202, 205, 206, 209, and 210 for SPS-2 experiments) within a given SPS experiment, as indicated in table 42. Figures 39 and 40 show the distribution of COV for all PCC and LCB analysis cells with complete and incomplete testing, respectively.

Table 45. Typical allowable variability for thickness data.

Data Item Reference Specimen Type COV, percent Standard Deviation

PCC Thickness Terzaghi et al.⁽²⁴⁾

PCC Thickness SHRP SPS-2⁽²⁵⁾ Cores 2 to 3¹

PCC Thickness Yoder and Witzak⁽²⁶⁾ Cores 2.5 to 12.5 mm

PCC Thickness Darter⁽²⁷⁾ Cores 3.1 to 3.2 8 mm

PCC Thickness Hoerner et al.⁽²⁸⁾ Cores 3.3 8 mm

PCC Thickness Neaman et al.⁽²⁹⁾ Cores 1.1 --

PCC Thickness McMahonet al.⁽³⁰⁾ Cores -- 7 mm

PCC Thickness Hughes et al.⁽³¹⁾ Cores 1.9 to 5.1 5 to 13 mm

PCC Thickness Lytton et al.⁽³²⁾ Cores 5 to 12 --

Data Item	Reference	Specimen Type	COV, percent	Standard Deviation
PCC Thickness	Terzaghi et al.⁽²⁴⁾
PCC Thickness	SHRP SPS-2⁽²⁵⁾	Cores	2 to 3¹
PCC Thickness	Yoder and Witzak⁽²⁶⁾	Cores		2.5 to 12.5 mm
PCC Thickness	Darter⁽²⁷⁾	Cores	3.1 to 3.2	8 mm
PCC Thickness	Hoerner et al.⁽²⁸⁾	Cores	3.3	8 mm
PCC Thickness	Neaman et al.⁽²⁹⁾	Cores	1.1	--
PCC Thickness	McMahonet al.⁽³⁰⁾	Cores	--	7 mm
PCC Thickness	Hughes et al.⁽³¹⁾	Cores	1.9 to 5.1	5 to 13 mm
PCC Thickness	Lytton et al.⁽³²⁾	Cores	5 to 12	--

¹Thicknesses must be within 6 mm of target values.

Figure 39. Distribution of standard deviation for pavement test sections thickness with complete testing.

Figure 40. Distribution of standard deviation for pavement section thickness with incomplete testing.

These figures show that 78 percent of the test data from cells with complete testing and 71 percent of the test data from cells with incomplete testing had standard deviations less than the typical value of 8 mm. Thus, inadequate testing did not seem to affect the variability of thickness data from multiple specimens within an analysis cell. The data from the analysis cells that showed more than typical variability were considered anomalous, and a more detailed examination of these data was performed to determine the reasons for the high variability.

Visual Examination Data Quality Evaluation

Visual examination was performed to determine the condition of the cores retrieved from the GPS and SPS experiments. Of the 4,209 core specimens at level E, 604 had no visual examination results and, hence, could not be evaluated. The remainder of the data was split into various categories and summarized based on the comment codes noted in each of the visual examination fields in the test table. A description of the comment codes entered in the TST_PC06 table is presented in table 46. The following categories were used in summarizing the information:

Category 1: No visual examination data available.
Category 2: Intact cores denoted by comment code 51. This code represents that the cores are in excellent condition and are suitable for testing.
Category 3: Intact cores denoted by comment codes 52, 53, 55, 56, and 65 through 76, in addition to comment code 51. These cores are also in excellent condition and suitable for testing but may contain some surface defects, such as voids in the matrix, uneven bottom, or segregation. Although these defects do not affect thickness measurement, they will need to be considered carefully if further testing is performed on these cores.
Category 4: These cores have hairline cracks, either on the surface or on the sides. These cores are in good condition otherwise and are suitable for testing. These cores have a comment code 52, in addition to other visual examination codes.
Category 5: Cores with comment codes 54, 57, or 64, which indicate badly damaged cores unsuitable for testing.
Category 6: Skewed cores indicated by comment code 82.
Category 7: All cores that do not fall in any of the six categories.

Figure 41 presents a piechart with the total number of records in each category expressed as a percentage of the total number of records considered in the analysis.

Approximately 78 percent of the cores are in categories 2, 3, and 4, indicating that they are suitable for testing. Note that "suitable for testing" does not necessarily mean that the core will provide consistent results when used in material characterization tests. The presence of hairline cracks, voids in the matrix, uneven core bottom, and material segregation each have an effect on material characterization. The degree to which these irregularities affect material characterization results varies, based on the sensitivity of the testing to these parameters. Therefore, it is important to consider the visual examination results appropriately when analyzing data from individual material characterization tests.

Fourteen percent of the records in the database were missing visual examination results, and 6 percent had comment code entries that were in the "miscellaneous" category. All the cores in the miscellaneous category were also suitable for testing based on the comment code entries, with the exception of four cores that showed signs of durability problems. Only 1 percent of the cores are unsuitable for testing due to damaged specimens, and an additional 1 percent are skewed cores.

Any data anomalies in the form of conflicting observations, such as describing cores as intact and in excellent condition, yet at the same time describing them as somehow cracked or damaged, were also checked. Only three core specimens from two different GPS sections had such conflicting visual examination results.

These specimens belong to a companion set of six specimens cored from each of these locations, and their thickness measurements were consistent with the other specimens. Hence, no remedial action was pursued. Further, considering that the numbers of specimens with such anomalies are in a minority, they are not expected to affect other material characterization results significantly.

Table 46. Description of visual survey codes.

Code Description

51 Intact core; excellent condition; suitable for testing

52 Hairline cracks on the surface of the core; suitable for testing

53 Cracks or voids visible along the side of the core; suitable for testing

54 Badly cracked or damaged core; unsuitable for testing

55 Ridges on the sides of the cores (identified by placing a straight edge along the side of the core when the distance between the straight edge and core face is 1.6 mm or greater); such a condition should be recorded if the core is used for any other test

56 Very rough and uneven bottom surface of the core (identified with this code when less than75 percent of the surface area is in contact with a level surface when the core is perpendicular to the surface)

57 Core extremely damaged from sampling, shipping, or laboratory handling; unsuitable for testing

58 Core was sawed in the laboratory to remove the core from the underlying bonded layer ofbase, subbase, or AC

59 Core consisted of two or more PCC layers; core was sawed in the laboratory and appropriate layer numbers were assigned to each PCC layer

60 One or more PCC layers have become separated; appropriate layer numbers were assigned toeach PCC layer

61 Segregation of coarse and fine aggregate is observed over 25 percent or more of the surface area of the core

62 Voids in the matrix of the PCC mixture are observed along the sides of the core

63 Voids due to loss of coarse and fine aggregate are observed along the sides of the core

64 Core is missing significant portions and cannot be considered a coherent cylindrical core;unsuitable for testing

65 Coarse aggregate along the face of the core contains 50 percent or more of crushed materials with fractured faces

66 Coarse aggregate along the face of the core is a mixture of uncrushed gravel and crushedgravel or stone

67 The exposed aggregates along the face of the core are lightweight aggregate

68 More than 10 percent of the surface area of the core contains soft and deleterious aggregateparticles or clay balls (soft aggregates are defined as those aggregates that can be easilyscratched with a knife)

69 Cracks are generally across or through the coarse aggregate

70 Cracks are generally around the periphery of the coarse aggregate

71 Cracks are associated with embedded steel

72 Rims are observed on aggregate

73 Fine aggregate is natural sand

74 Fine aggregate is manufactured sand

75 Fine aggregate is a mixture of natural and manufactured sand

76 Steel is present in the core (give type size and location of steel in a separate note)

77 Steel is corroded

78 Core indicates D-cracking (a series of closely spaced crescent-shaped hairline cracks that appear at a PCC pavement surface at longitudinal joints/cracks and transverse joints/cracks)

79 Core indicates deterioration due to freeze-thaw cycles

80 Core indicates sulfate attack

81 Core indicates alkali silica reactivity (expansion of reactive aggregates)

82 Skewed core (when either end of the core departs from perpendicularity to the axis by more than 0.5 degrees or 3 mm in 300 mm, as tested by placing the core on a level surface)

99 Other comment (describe in a note)

Code	Description
51	Intact core; excellent condition; suitable for testing
52	Hairline cracks on the surface of the core; suitable for testing
53	Cracks or voids visible along the side of the core; suitable for testing
54	Badly cracked or damaged core; unsuitable for testing
55	Ridges on the sides of the cores (identified by placing a straight edge along the side of the core when the distance between the straight edge and core face is 1.6 mm or greater); such a condition should be recorded if the core is used for any other test
56	Very rough and uneven bottom surface of the core (identified with this code when less than75 percent of the surface area is in contact with a level surface when the core is perpendicular to the surface)
57	Core extremely damaged from sampling, shipping, or laboratory handling; unsuitable for testing
58	Core was sawed in the laboratory to remove the core from the underlying bonded layer ofbase, subbase, or AC
59	Core consisted of two or more PCC layers; core was sawed in the laboratory and appropriate layer numbers were assigned to each PCC layer
60	One or more PCC layers have become separated; appropriate layer numbers were assigned toeach PCC layer
61	Segregation of coarse and fine aggregate is observed over 25 percent or more of the surface area of the core
62	Voids in the matrix of the PCC mixture are observed along the sides of the core
63	Voids due to loss of coarse and fine aggregate are observed along the sides of the core
64	Core is missing significant portions and cannot be considered a coherent cylindrical core;unsuitable for testing
65	Coarse aggregate along the face of the core contains 50 percent or more of crushed materials with fractured faces
66	Coarse aggregate along the face of the core is a mixture of uncrushed gravel and crushedgravel or stone
67	The exposed aggregates along the face of the core are lightweight aggregate
68	More than 10 percent of the surface area of the core contains soft and deleterious aggregateparticles or clay balls (soft aggregates are defined as those aggregates that can be easilyscratched with a knife)
69	Cracks are generally across or through the coarse aggregate
70	Cracks are generally around the periphery of the coarse aggregate
71	Cracks are associated with embedded steel
72	Rims are observed on aggregate
73	Fine aggregate is natural sand
74	Fine aggregate is manufactured sand
75	Fine aggregate is a mixture of natural and manufactured sand
76	Steel is present in the core (give type size and location of steel in a separate note)
77	Steel is corroded
78	Core indicates D-cracking (a series of closely spaced crescent-shaped hairline cracks that appear at a PCC pavement surface at longitudinal joints/cracks and transverse joints/cracks)
79	Core indicates deterioration due to freeze-thaw cycles
80	Core indicates sulfate attack
81	Core indicates alkali silica reactivity (expansion of reactive aggregates)
82	Skewed core (when either end of the core departs from perpendicularity to the axis by more than 0.5 degrees or 3 mm in 300 mm, as tested by placing the core on a level surface)
99	Other comment (describe in a note)

Figure 41. Summary of visual examination comments for all core specimens.

Figure 41. Summary of visual examination comments for all core specimens. In a pie chart, the figure shows that Category 3 corresponds to 64%, Category 1 14%, Category 2 8%, Categories 4 and 7 6% each, Categories 5 and 6 1% each.

Identification of Anomalous Data

The anomalies noted during the various analyses are described below, along with a discussion of potential causes for their occurrence. Where possible, corrective or remedial actions were undertaken to address the anomalous data. It should be noted that excessive variability in the thickness values from multiple specimens belonging to the same analysis cell was the primary factor used in identifying anomalous data.

Anomaly 1: Mismatch in Target versus Actual Thicknesses

The reasonableness of experiments without a target project thickness value was ascertained by verifying whether the range of thicknesses found match that commonly encountered in practice. Where target thicknesses were available, reasonableness was ascertained by comparing the mean and standard deviation of the thickness information in the test table with the target values. Although data from SPS-2 and SPS-8 experiments were reasonable, the mean thickness and range of thicknesses from the various projects within the SPS-7 experiment were not consistent with the target values. Upon further analysis, it was revealed that the cause for this anomaly was the presence of "broken cores" and the fact that one of the States constructed overlays thicker than specified.

All "broken" or short cores leading to thickness data variability or bias were not used in further analysis. Removal of such data from further analysis meant that they were not included in developing the representative test tables. Because constructing a thicker-than-specified overlay is not an anomaly in itself, no remedial action is suggested for such data. However, the user should pay particular attention to this set of data if target thickness is a parameter of interest in the analysis under consideration.

Anomaly 2: Single Test Values

Eight analysis cells contained data from only a single test specimen. A single test value could result from inadequate sampling, discarded cores due to excessive damage, or a combination thereof. It is, therefore, not a testing anomaly. Nevertheless, the absence of duplicate test values to corroborate the measurements makes it difficult to characterize PCC layer thickness with a great degree of confidence.

To verify the accuracy of the single point thickness measurements, they were compared with corresponding TST_L05B representative layer thickness information in the LTPP database. The TST_L05B table uses more than one source (e.g., elevation information, thickness data from cores, bulk samples, test pits) while assigning representative layer thicknesses. The comparisons revealed that thickness data from six analysis cells matched the TST_L05B information. These data were, therefore, retained in the database, with an appropriate comment denoting that these are single test values. The TST_L05B table did not contain releasable data for the remaining two analysis cells, so the validity of these data could not be verified. Pending forensic testing to validate these thicknesses, these data were also retained in the database with an appropriate comment. The comment will inform users that the representative PCC layer thickness value assigned was from a single test.

Anomaly 3: Excessive Variability

Analysis cells with complete testing and incomplete testing were combined for the purposes of this evaluation. From a total of 607 analysis cells, 142 cells with multiple test values showed excessive within-cell variabilities (greater than 8 mm standard deviation). This constitutes approximately 23 percent of the total cells in the database considered for analysis. A careful review of the various data fields, including the comment fields, was performed to understand the reasons for the high within-cell variabilities. Based on this evaluation, the excess within-cell variability was attributed to the following causes:

Presence of broken or short cores with significantly different core thicknesses than the companion specimens
Presence of outliers

Data from broken or short cores were not used in developing the representative test values. Representative statistics computed without such data were compared with the original computed variability to observe the effect of removal of these data from the analysis. The 19 records from broken or short cores all belong to test sections within the SPS-7 experiment. They affected a total of eight analysis cells.

Standard deviations computed for seven of the affected eight cells without the affected test data were less than the typical value. For the remaining analysis cell, the recomputed standard deviation was only about 1.5 mm higher than the typical value. The computed representative test values and associated statistics were included into the representative data table with comments indicating the presence of the anomaly, the type of remedial action performed, and the effect the remedial action had on reducing the within-cell variability.

The 135 remaining analysis cells with excess variability were examined statistically to determine any outlying observations or outliers because the excessive variability could not be attributed to any physical causes. The ASTM E178 procedure entitled Standard Practice for Dealing with Outlying Observations was used for outlier identification (assuming a significance level of 0.05).⁽³⁾

Using the procedure outlined in ASTM E 178, a total of 22 analysis cells were detected as having outlier entries. The core thicknesses deemed as outliers were removed from the affected analysis cells, and the representative statistics were recomputed. This resulted in acceptable thickness variabilities for 20 of the 22 analysis cells. The two cells that did not meet the criteria had variabilities only slightly in excess of the acceptable 8 mm. The recomputed statistics were entered into the representative data set with comments indicating the presence of outliers, the type of remedial action performed, and the effect the remedial action had on reducing the within-cell variability.

Remedial Action--Summary

After taking appropriate remedial actions to account for inaccurate thickness data entries caused by broken or short cores and removal of outlier data entries, 27 of the 142 analysis cells originally exhibiting excessive variability were found to have acceptable within-cell thickness variability. For the remaining 115 analysis cells, no plausible explanation was found for the high within-cell thickness data variability. These data were retained in the representative data set with an appropriate comment code indicating the presence of excess variability.

Schema for Table TST_PC06_REP--Representative Length Measurements for PCC and LCB Cores

Representative length measurements for PCC and LCB cores for each analysis cell were computed along with related statistics and recommended for inclusion into the LTPP database. The schema developed for the representative thickness data table is presented in the next section. Note that this table summarizes essentially only the core thickness information. Summarizing visual examination information on an analysis cell basis was impractical.

However, as indicated earlier, the visual examination data revealed very few anomalies in the data, which was considered in developing the representative test values for each analysis cell. Also, the visual examination results showed that most of the cores were suitable for additional laboratory testing (e.g., compressive strength testing). The following data are maintained in the thickness data set:

Experiment type.
Experiment number.
SHRP identification number.
State code.
Construction number.
Layer number.
Material code.
Number of specimens tested.
Mean thickness.
Maximum thickness.
Minimum thickness.
COV of thickness data.
Standard deviation of thickness data.
Data source.
QA_Comment_1.
QA_Comment_2.
QA_Comment_3.
QA_Comment_4.
QA_Comment_5.
QA_Comment_6.
QA_Comment_Other.
Record status.

The first seven fields in the schema define the analysis cell. Field 8 presents the number of test specimens with thickness data within each analysis cell. The representative test value (mean thickness), along with the range of the thicknesses (max and min), COV, and standard deviation on an analysis cell basis are presented in fields 9 through 13. Field 14 describes the source of the data (all the values reported are original test values, i.e, no data were calculated).

Finally, comments are provided in fields 15 through 21 to describe the quality status of the data (e.g., excessive within-cell variability, incomplete sampling), based on the QA testing performed as part of this discussion to guide the user in selecting data for analysis and evaluation. The last field describes the record status.

10. DETERMINATION OF COMPRESSIVE STRENGTH OF PORTLAND CEMENT CONCRETE CORES

Introduction

The compressive strength of PCC is one of the most important properties widely used for design, research, and QC during construction. It is an indicator of the stress required to cause failure of a test specimen. A higher compressive strength indicates a stronger material. In addition to being an indicator of load-carrying ability, compressive strength also indicates, either directly or indirectly, resistance to wear, permeability, and durability. Because compressive strength testing can be performed with relative ease, its results are often used in QA/QC and to estimate concrete properties such as the flexural strength, elastic modulus, and tensile strength.

The determination of the compressive strength of PCC materials under the LTPP program covers jointed plain, jointed reinforced, and continuously reinforced concrete pavements, as well as concrete overlays and LCB's (from the SPS-2 experiment). Specimens are tested in accordance with SHRP protocol P61, which is based on the test standard AASHTO T22-88 (ASTM C39).

The test results are stored in the LTPP database after undergoing several levels of QA/QC checks that ensure a reasonable quality of data. Data that are of acceptable quality after the final series of QA/QC checks are classified as level E and are stored in table TST_PC01 in the LTPP database. Information maintained under this table for each specimen tested includes:

SHRP identification number.
State code.
Layer number.
Field set.
Test number.
Location number.
Construction number.
Sample number.
Laboratory code.
Sample area number.
Test date.
Diameter.
Original length.
Capped length.
Length/diameter (L/D) ratio.
Cross-section area.
Compressive strength.
Maximum load.
Compressive strength fracture type code.
Compressive strength fracture type (other).
Comment codes for testing (seven fields).
Record status.

Material Sampling for Compressive Strength Testing

Test samples or specimens used for compressive strength testing consist of either cylinders cast from samples taken during construction or cores drilled from in-place PCC slabs. After the samples were procured from the field, they are carefully packaged, labeled, and shipped for testing, following the procedures outlined in the SHRP P61 protocol.

In general, two samples (one from either end of the test section but outside the monitoring area) were cored for GPS experiments for compressive strength testing. For SPS projects, the sampling plan was experiment-specific and often involved collecting samples from either end of test sections within a given experiment. Because some of the SPS experiments involve new construction or overlay construction (new pavement or overlays), fresh concrete cylinder specimens were also obtained during construction from test sections within these experiments. A detailed overview of the sampling locations for each SPS experiment is provided in the SPS Experimental Design and Research Plan documents developed by SHRP. (See references 17, 18, and 19 through 23.)

PCC compressive strength test data within the LTPP database table TST_PC01 pertain to GPS experiments 3, 4, 5, 7, and 9 and SPS experiments 2, 6, 7, and 8. Testing was performed on core specimens from all GPS and SPS-6 sections, in addition to cored samples from original pavement sections within the SPS-7 experiment. For SPS-2, SPS-7, and SPS-8 new and overlay test sections, testing was performed on both cylinders (fresh concrete) molded during construction for the newly placed concrete layers and cores taken after construction. Further, for the SPS-2 sections, cylinders and cores were obtained for both the PCC surface and LCB layers.

Table 47 presents the sampling program to determine the compressive strength of PCC materials. The table indicates the minimum number of tests required for each layer type for the relevant LTPP experiments, along with the sample locations. For GPS experiments, the number of tests required is strictly based on layer type, whereas for SPS-2, 7, and 8 experiments, it is also based on the target strength, specimen age, and specimen type (cylinder or core). This presents some unique challenges in the definition of analysis cells and the analysis of data in TST_PC01.

Table 47. Sampling requirements for determination of compressive strength of PCC materials.

Expt. Type Layer Type Test Type LTPP Designation LTPP Protocol Min. No. of Tests per Layer Designated Sampling Locations

GPS-3, 4, 5, and 9 PCC Comp. strength PC01 P61 2 C2 C8

GPS-7 PCC Comp. strength PC01 P61 2 C8 C20

SPS-2 PCC Comp. strength
(14 day, 3.8 MPa) PC01 P61 3 (cylinder) B21-B23

SPS-2 PCC Comp. strength
(14 day, 3.8 MPa) PC01 P61 6 (core) C1 C10 C18 C26 C34 C43

SPS-2 PCC Comp. strength
(14 day, 6.2 MPa) PC01 P61 3 (cylinder) B24-B26

SPS-2 PCC Comp. strength
(14 day, 6.2 MPa) PC01 P61 6 (core) C51 C59 C67 C75 C83 C91

SPS-2 PCC Comp. strength
(28 day, 3.8 MPa) PC01 P61 3 (cylinder) B21-B23

SPS-2 PCC Comp. strength
(28 day, 3.8 MPa) PC01 P61 6 (core) C2 C11 C19 C27 C35 C44

SPS-2 PCC Comp. strength
(28 day, 6.2 MPa) PC01 P61 3 (cylinder) B24-B26

SPS-2 PCC Comp. strength
(28 day, 6.2 MPa) PC01 P61 6 (core) C52 C60 C68 C76 C84 C92

SPS-2 PCC Comp. strength
(1 year, 3.8 MPa) PC01 P61 3 (cylinder) B21-B23

SPS-2 PCC Comp. strength
(1 year, 3.8 MPa) PC01 P61 6 (core) C3 C12 C20 C28 C36 C45

SPS-2 PCC Comp. strength
(1 year, 6.2 MPa) PC01 P61 3 (cylinder) B24-B26

SPS-2 PCC Comp. strength
(1 year, 6.2 MPa) PC01 P61 6 (core) C53 C61 C69 C77 C85 C93

SPS-2 LCB 7 day PC01 P61 6 (cylinder) B19 B20

SPS-2 LCB 14 day PC01 P61 8 (core) C18 C22 C26 C30 C67 C70 C75 C79

SPS-2 LCB 28 day PC01 P61 6 (cylinder) B19 B20

SPS-2 LCB 28 day PC01 P61 8 (core) C19 C23 C27 C31 C68 C71 C76 C80

SPS-2 LCB 1 year PC01 P61 6 (cylinder) B19 B20

SPS-2 LCB 1 year PC01 P61 8 (core) C20 C24 C28 C32 C69 C72 C77 C81

SPS-6 PCC
(cores) Comp. strength PC01 P61 10 C1 C3 C5 C7 C9 C11 C13 C15 C17 C19

SPS-7 PCC--Overlay
(Postconstruction cores) Comp. strength 14 day PC01 P61 4 C12 C15 C17 C19

SPS-7 PCC--Overlay
(Postconstruction cores) Comp. strength 28 day PC01 P61 4 C43 C50 C57 C64

SPS-7 PCC--Overlay
(Postconstruction cores) Comp. strength 365 day PC01 P61 4 C86 C93 C100 C107

SPS-7 PCC--Overlay
(During construction cylinders) Comp. strength 14 day PC01 P61 6 FC1 FC2 FC3 (75-mm cores)

SPS-7 PCC--Overlay
(During construction cylinders) Comp. strength 14 day PC01 P61 6 FC4 FC5 FC6 (125-mm cores)

SPS-7 PCC--Overlay
(During construction cylinders) Comp. strength 14 day PC01 P61 6<

Expt. Type	Layer Type	Test Type	LTPP Designation	LTPP Protocol	Min. No. of Tests per Layer	Designated Sampling Locations
GPS-3, 4, 5, and 9	PCC	Comp. strength	PC01	P61	2	C2 C8
GPS-7	PCC	Comp. strength	PC01	P61	2	C8 C20
SPS-2	PCC	Comp. strength (14 day, 3.8 MPa)	PC01	P61	3 (cylinder)	B21-B23
SPS-2	PCC	Comp. strength (14 day, 3.8 MPa)	PC01	P61	6 (core)	C1 C10 C18 C26 C34 C43
SPS-2	PCC	Comp. strength (14 day, 6.2 MPa)	PC01	P61	3 (cylinder)	B24-B26
SPS-2	PCC	Comp. strength (14 day, 6.2 MPa)	PC01	P61	6 (core)	C51 C59 C67 C75 C83 C91
SPS-2	PCC	Comp. strength (28 day, 3.8 MPa)	PC01	P61	3 (cylinder)	B21-B23
SPS-2	PCC	Comp. strength (28 day, 3.8 MPa)	PC01	P61	6 (core)	C2 C11 C19 C27 C35 C44
SPS-2	PCC	Comp. strength (28 day, 6.2 MPa)	PC01	P61	3 (cylinder)	B24-B26
SPS-2	PCC	Comp. strength (28 day, 6.2 MPa)	PC01	P61	6 (core)	C52 C60 C68 C76 C84 C92
SPS-2	PCC	Comp. strength (1 year, 3.8 MPa)	PC01	P61	3 (cylinder)	B21-B23
SPS-2	PCC	Comp. strength (1 year, 3.8 MPa)	PC01	P61	6 (core)	C3 C12 C20 C28 C36 C45
SPS-2	PCC	Comp. strength (1 year, 6.2 MPa)	PC01	P61	3 (cylinder)	B24-B26
SPS-2	PCC	Comp. strength (1 year, 6.2 MPa)	PC01	P61	6 (core)	C53 C61 C69 C77 C85 C93
SPS-2	LCB	7 day	PC01	P61	6 (cylinder)	B19 B20
SPS-2	LCB	14 day	PC01	P61	8 (core)	C18 C22 C26 C30 C67 C70 C75 C79
SPS-2	LCB	28 day	PC01	P61	6 (cylinder)	B19 B20
SPS-2	LCB	28 day	PC01	P61	8 (core)	C19 C23 C27 C31 C68 C71 C76 C80
SPS-2	LCB	1 year	PC01	P61	6 (cylinder)	B19 B20
SPS-2	LCB	1 year	PC01	P61	8 (core)	C20 C24 C28 C32 C69 C72 C77 C81
SPS-6	PCC (cores)	Comp. strength	PC01	P61	10	C1 C3 C5 C7 C9 C11 C13 C15 C17 C19
SPS-7	PCC--Overlay (Postconstruction cores)	Comp. strength 14 day	PC01	P61	4	C12 C15 C17 C19
SPS-7	PCC--Overlay (Postconstruction cores)	Comp. strength 28 day	PC01	P61	4	C43 C50 C57 C64
SPS-7	PCC--Overlay (Postconstruction cores)	Comp. strength 365 day	PC01	P61	4	C86 C93 C100 C107
SPS-7	PCC--Overlay (During construction cylinders)	Comp. strength 14 day	PC01	P61	6	FC1 FC2 FC3 (75-mm cores)
SPS-7	PCC--Overlay (During construction cylinders)	Comp. strength 14 day	PC01	P61	6	FC4 FC5 FC6 (125-mm cores)
SPS-7	PCC--Overlay (During construction cylinders)	Comp. strength 14 day	PC01	P61	6<