• QC/QA: Once data are input into the IMS and reviewed by regional engineers, formal QC/QA software programs are run on the data.
• Level A—Random checks of data are performed to ensure correct RIMS to National IMS (NIMS) data transfer.
• Level B—A set of dependency checks are performed to ensure that basic essential section information has been recorded in the IMS. In addition, experiment types are verified based on inventory data. These checks are currently in the process of incorporation into the Level E checks for all modules.
• Level C—A minimum data search is performed for critical elements, e.g., verification that inventory data contains the coordinates of the section, that friction data contains the skid number, and that rehabilitation data has a code entered to identify each work type activity.
• Level D—Expanded range checks are applied to certain fields to identify data element values that fall outside an expected range. These checks are more stringent than the input range checks reviewed by the regional engineers.
• Level E—Intramodular checks are employed to verify the consistency of data within a data module, e.g., if an overlay is identified in the inventory layer structure, the data of the overlay should be recorded in the inventory table listing major improvements to the pavement structure.

When the QC/QA programs are completed, the regional engineers review the output and resolve any data errors. Often the data entered are legitimate and accurate, but do not pass a QC/QA check. If this occurs, the regional engineer can document that the data have been confirmed using a comments table in the IMS and can manually upgrade the record to Level E.

Figure 2 shows the movement of data elements and quality checks completed on the data prior to release to the public. Only a fraction of the data fields are checked. A value of A is assigned automatically to a record on entry in the database. A value of B indicates the QC process was executed and a Level C check failed. Any record for which correct section information is stored in the database is available after the QC is completed. A record of the QC processing is included with the record. Since the checks are run in sequence A-E, the last successful check is identified on the record as the record status variable.  A value of B or C indicates that a necessary data element was not available when the QC was processed and does not necessarily imply that the higher level QC was unsuccessful.

There are numerous reasons why some data may be unavailable from the publicly released IMS database at the time the data were actually collected.  Following are some examples:

• Data are not yet collected.
• Data are under regional review.
• Data have failed one of the quality checks and are to be reviewed.
• Data have failed one of the quality checks and were identified as anomalies.
• Data are not yet quality checked.

Therefore, the missing data identified in this report do not necessarily mean that the data were not collected or submitted by the States.  There are several places where data may be delayed and not reach Level E.  The results in this report are based only upon Level E because it was impossible to know the specific reasons why that data did not pass all of the QC checks.  Many of the reasons that prevent data from reaching Level E status are not the result of poor quality or unreliability of the data.  The LTPP program is embarking on a systemwide effort to resolve all unavailable data so that future researchers can access them.

DATA ELEMENT CATEGORIES

All of the data elements included in the SPS-1 experiment were reviewed for their availability and completeness in the LTPP database as listed in table 5.  The data elements were divided into three categories for the review process—essential, explanatory, and informational.  Each category is defined briefly below.

• Essential—Data elements that are needed to accomplish the SPS-1 experimental objectives and expectations.  Without these data elements, the experiment will not accomplish its intended function.
• Explanatory—Data elements that are not necessary to achieve the experimental expectations, but are needed to explain any differences or anomalies in the performance observations.
• Informational—Data elements that are not needed or required to achieve the experimental objectives.  These data elements would only provide information that may be needed for future and more generalized studies.

 Although the review of the SPS-1 experiment included all data elements, the detailed review concentrated on those elements that were identified as essential and explanatory.  The key data elements that were evaluated and assessed for determining the quality level and completeness for each project were subdivided into the following types of data, and are discussed in this chapter:

• General Site Information.
• Pavement Structure.
• Construction Data.
• Monitoring Data.
• Materials Data.
• Traffic Data.
• Climate.
  

Table 5.  Summary of SPS-1 data elements and their importance to experimental expectations.

 *Data Availability—percentage of SPS-1 required testing for which data generally are available in the database at Level E.

GENERAL SITE INFORMATION

This assessment includes the site identification and location, key equipment installed at the site, the construction report’s availability, and important dates associated with each of the SPS-1 projects.  The information for this review was obtained from the site construction report, deviation report, or from the IMS tables entitled EXPERIMENT_SECTION and SPS_ID.  All of the site level records for the 18 constructed SPS-1 projects are at Level E.  These data records are complete, as noted in the project summary records presented in appendix A.  Table 6 includes a summary of the site information and report availability for each of the projects.

Construction and deviation reports were available for review from all of the projects except Michigan, Wisconsin, and Montana.  Montana and Wisconsin are new projects, while the Michigan project is 4 years old.  The construction report for the Montana project has been drafted, but is awaiting additional construction information before submittal to LTPP and the Wisconsin construction report was submitted to LTPP after the review had been completed.

AWS equipment has been installed at all sites.  However, WIM and Automated Vehicle Classification (AVC) equipment has not been installed at five of the project sites: Alabama, Delaware, Louisiana, Oklahoma, and New Mexico (see table 6).  This is considered significant to the experiment, especially when trying to validate the more sophisticated mechanistic-empirical design-analysis procedures.  Specifically, reliable and site-specific traffic data are considered vital to National Cooperative Highway Research Program (NCHRP) Project 1-37A, development of the 2002 Guide for the Design of New and Rehabilitated Pavement Structures.

 Table 6. SPS-1 project site information and report availability.

Notes: 

DESIGN VERSUS ACTUAL CONSTRUCTION REVIEW

Chapter 3 presented a summary of the construction and specification requirements for each of the SPS-1 projects.  Additionally, the Nomination Guidelines ⁽¹¹⁾ and Construction Guidelines ⁽¹²⁾ for FHWA’s Guidelines for Nomination and Evaluation of Candidate Projects for Experiment SPS-1 Strategic Study of Structural Factors for Flexible Pavements also established specific site selection criteria and key variable construction guidelines.  The guidelines presented in both of these reports were developed to control quality and integrity of the SPS-1 experiment results and findings.  Therefore, they should be considered in the construction adequacy evaluation and assessment.

One of the main objectives of this study was to identify any confounding factors introduced into the SPS-1 experiment regarding construction deviations or other factors not accounted for in the original experiment design. It is extremely important to evaluate the types of variables that are considered key design factors in the SPS-1 experiment and to determine if any deviation of the design parameters established for the design factorial will adversely affect the experimental expectations.

This section of the report evaluates the design versus the actual construction of key variables identified within the experimental factorial and the above-mentioned experiment guidelines.

Subgrade Soil

The type of subgrade soil is a key factor in the experimental design.  Specifically, the SPS-1 experimental design called for half of the projects to be constructed on coarse-grained soils and the other half to be built over fine-grained soils.  An additional requirement of the experiment was that all test sections at a site be constructed on the same type of soil (i.e., the same soil classification).  Table 7 provides a summary of the subgrade soils and their classification in comparison to the original nomination (refer to table 1).  As tabulated, only one of the sites (Texas) is now listed within a different experimental cell because the subgrade soils were found to be different than originally nominated.

Similarly, the subgrade soils on which these projects were built are relatively consistent for each of the core test sections at a site.  In fact, there are only two projects where the subgrade classification varies between the different test sections at a project—Kansas and New Mexico.  The test sections with the different soil classifications are noted in table 7 and show that 5 of the 18 test sections in Kansas are classified as coarse-grained subgrades, while only 1 of the 12 test sections in New Mexico is classified as coarse-grained.  This subgrade variation is considered typical, and it is not believed that this deviation from the experiment requirement will have a detrimental impact in achieving the expectations of the SPS-1 experiment.

One major discrepancy was noted during the review process.  All subgrades are classified by the RCO and this classification is entered into the SPS1_LAYER table, as shown in table 7. In some cases, this classification is different from the soil type identified on table TST_L05B.  For example, Kansas, Nevada, and Texas have different classifications between tables TST_L05B and SPS1_LAYER.  Thus, an additional check should be added to cross-reference the subgrade soil classification between the TST_L05B and SPS1_LAYER tables to ensure that the same data elements are consistent.

Climate

The SPS-1 experimental design called for each project to be located in one of four different climates: wet-freeze, wet-no-freeze, dry-freeze, or dry-no-freeze. The main purpose of this factor was to obtain SPS-1 projects in different climates, as well as a geographical distribution across the United States and Canada.  Figure 1 provided a summary of the geographical distribution of these projects across the United States and Canada.  Table 8 tabulates the average annual rainfall, mean annual air temperatures, and freezing index that have been measured, which define each site’s climatic zone.

Table 7.  SPS-1 subgrade classification.

The general climatic data include actual measurements from at least one nearby weather station for each LTPP site. In addition, a site-specific statistical estimate, based on as many as five nearby weather stations, is available for each project.  These estimates are called virtual weather stations.  The IMS contains monthly and average annual summary statistics.  Daily data for both the virtual weather stations and actual weather stations are kept off-line.  General environmental data available in the IMS are derived from weather data originally collected from the NOAA.

AWS equipment is installed at every SPS-1 project site (refer to table 6). The AWS provides site-specific information for the same parameters as the general environmental tables, but these data are available with monthly, daily, or hourly statistics.  The number of days from the AWS at each project site is summarized in table 8.  An appreciable amount of climatic data has been collected from the AWS.

The SPS-1 project sites include a wide range of freezing index, temperatures, and annual rainfall, as originally planned.  Those sites with an average annual rainfall greater than 1,000 mm are classified as wet and those sites with less than 1,000 mm are classified as dry.  Similarly, the sites with a freezing index greater than 60 ºC-days would be classified as a freezing climate and those with less than 60 ºC-days would be designated as a no-freeze climate. It should be noted that the values used to determine the specific climatic cell assignment are arbitrary and only used to ensure that the projects cover a diverse range of climates.  An annual rainfall of 1,000 mm was used in some of the earlier LTPP studies, while an annual rainfall of 508 mm is used in the latest version of DATAPAVE^® for designating the site as wet or dry.  A freezing index value of 60 °C-days was used to determine whether the site falls into a no-freeze or freeze cell while a different value is used in DATAPAVE.

Using these definitions, some sites do not appear to be in the correct experimental cells.  For example, Iowa, Michigan, and Ohio all have average annual rainfalls less than 1,000 mm, but are in the experimental cells designated as a wet climate. It is expected that the average rainfall at the project sites will increase with time.  Similarly, Virginia was originally nominated for a freezing climate but has an average freezing index of 38 °C-days since construction. It is expected that the average freezing index at this site will increase over time.

All sites are in compliance with the appropriate cell requirements based on the NOAA and historical data.  As a result, the climate designations have not been changed on the basis of a few years’ worth of data.  These relatively small differences in the average rainfall and freezing index are not considered detrimental to achieving the SPS-1 experimental objectives or expectations.  The experimental sites still represent a diverse range of climates across the United States and Canada, as originally planned.

Layer Thickness/Structure

The pavement structure data are divided into two elements—layer data and design features. Important general design features such as drainage, lane width, and shoulder type are included in table SPS_GENERAL.  All of the key design feature data are available for all of the SPS-1 test sections, and all are at Level E.

The pavement layer data for the SPS-1 test sections are available from two different sources.  These two sources include the rod and level measurements (IMS Table SPS1_LAYER) and thicknesses from the cores recovered on-site (IMS Table TST_L05B).  Both of these tables were examined to evaluate the thickness measurements and variation of the layer thickness data for each of the structural layers within the SPS-1 cross-sections.  The average thickness of each layer is provided in appendix B for all of the projects for which data are available.  The TST_L05B table contains records for all layers for 17 of the 18 projects.  Layer information on Wisconsin has yet to become available because this project is new and the data have not undergone the QC process. 

The SPS1_LAYER table contains all layer data for the 14 SPS-1 projects that are at Level E.  The projects from which construction data do not exist are Wisconsin, Michigan, Montana, and Nebraska. The Montana and Wisconsin projects are relatively new; the data have been collected, but have not passed the entire QC process

In general, the average layer thicknesses for each layer were as originally planned within the construction guidelines for the SPS-1 experiment.  The one construction element that was not satisfied included the layer thickness deviations from the planned thickness within the experiment. On every test section and project, the variation of the layer thicknesses was greater than the maximum value identified in the construction guidelines (refer to chapter 3). It is believed that the construction guidelines called for a tolerance that was impractical.

Histograms for each layer type and thickness level were prepared to review the distribution of layer thicknesses for all projects.  Examples of these histograms are included in figures 3 through 10.  Each figure includes the distribution of layer thicknesses as included in table TST_ L05B and from the construction data or table SPS1_LAYER.  As shown, the distributions between the different thickness methods are very similar, and the average values from those thickness determination methods are approximately equal.  These thickness variations (or histograms) represent typical construction practices, and all data sets are normally distributed (with the possible exception of the thin [102-mm] DGAB layer).  This variation of layer thickness, which is greater than required by the construction guidelines, is not believed to be a detriment to the experiment or to prevent the experimental objectives from being met.  None of the thickness data sets for the same material overlap (e.g., 102 mm versus 178 mm for the HMA layers).

The pavement cross-section and material types planned for each test section within the core experiment of each project were generally met and adhered to based on the construction guidelines.  The only deviation to the planned cross-sections was for the Iowa project, where a DGAB layer was placed beneath the PATB layer on one of the test sections.  This is not believed to have a significant effect on the experiment.

MATERIALS TESTING

Field and laboratory tests were conducted to establish the properties of each material included in the SPS-1 experiment.  The material properties and the variation of those properties, both between and within the test sections, are required to evaluate and explain causes of performance differences between the test sections.  Many of these properties or material characteristics are those that are currently used in existing pavement design and analysis methods.

The material sampling and testing requirements are documented in the SPS-1 materials sampling and testing guidelines report.⁽⁴⁾  This report contains the development of the SPS-1 sampling and testing plans, field material sampling and testing requirements, and laboratory materials testing requirements for each SPS-1 project site.  SPS-1 materials sampling and testing plans for the subgrade and base materials are provided in chapter 3. In addition, the testing requirements for each of the materials are designated in appendix A.

Figure 3.   Thickness histograms for the thin HMA layer (102 mm) from  tables SPS1_LAYER (construction data) and TST_L05B.

Figure 4.  Thickness histograms for the thick HMA layer (178 mm) from tables SPS1_LAYER (construction data) and TST_L05B.

Figure 5.   Thickness histograms for the thin ATB layer (102 mm) from tables SPS1_LAYER (construction data) and TST_L05B.

Figure 6.    Thickness histograms for the thick ATB layer (203 mm) from tables SPS1_LAYER (construction data) and TST_L05B.

Figure 7.  Thickness histograms for the PATB layer from tables  SPS1_LAYER (construction data) and TST_L05B.

 

Figure 8.  Thickness histograms for the 102-mm DGAB layer from tables
 SPS1_LAYER (construction data) and TST_L05B.

 
Figure 9.  Thickness histograms for the 203-mm DGAB layer from tables
SPS1_LAYER (construction data) and TST_L05B.

 

Figure 10. Thickness histograms for the 305-mm DGAB layer from tables
SPS1_LAYER (construction data) and TST_L05B.

Tables 9 through 13 summarize the available test data from selected tests by material type for each of the projects while table 14 provides a summary of the overall materials testing completed for the core test sections.  As shown, there is still a substantial amount of testing that needs to be completed to fill the experiment, even for those data elements or material properties identified as essential (refer to table 5). If this testing is not completed (at least for the essential data elements), the missing laboratory test results from most of the SPS-1 projects will have a detrimental impact on the experiment for achieving the experimental objectives and expectations.

To evaluate the relative difference in construction or the in-place properties, histograms of different material properties were prepared.  Figures 11 through 13 show the variation of air voids in the different HMA and ATB layers.  As shown, these variations are substantial enough to cause a significant difference in performance. In fact, some of the air voids are greater than 10 percent, which indicates inadequate compaction or other mixture problems.  These differences in air voids need to be considered and accounted for in any evaluation or analysis of the performance data.

The material test data that are available were further reviewed to evaluate other material and construction variations between and within the different cells of the experiment.  Figures 14 and 15 show the gradation test results for the percentage passing the number 4 and 200 sieves for the PATB material.  As illustrated, there are only a small percentage of the tests where the measured gradation may significantly restrict the layer’s capacity to remove any surface water infiltration quickly.  Figures 16 and 17 show other typical examples of the variability in the percentage passing the number 200 sieve for the HMA surface and ATB layers that exists in this SPS-1 experiment. 

This variability is typical for the other materials using standard construction practices for each specific material.  These test results suggest that the materials used in construction have similar physical properties.

TRAFFIC

Traffic data provide estimates of annual vehicle counts by vehicle classification and distribution of axle weights by axle type.  Annual traffic summary statistics are stored in the IMS traffic module, when available.  These data are supposed to be provided for each year after the roadway was opened to the traffic.  For the SPS-1 experiment, traffic data are collected at the project site using a combination of permanent and portable equipment by the individual States and Provinces.

The SPS-1 experiment design calls for continuous WIM monitoring, as permitted by WIM scale operating divisions.  Table TRF_MONITOR_BASIC_INFO was examined to identify the SPS-1 records with WIM, AVC data, and annual ESAL estimates.  The availability of WIM and AVC was further classified as “at least 1 day” or “continuous.”

 Table 9.  Summary of materials testing on the subgrade soils.

 Table 10.  Summary of materials testing on the unbound aggregate base materials.