National Academies Press: OpenBook
« Previous: Chapter 3: Findings and Applications
Page 135
Suggested Citation:"Chapter 4: Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2021. Investigating the Relationship of As-Constructed Asphalt Pavement Air Voids to Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/26219.
×
Page 135
Page 136
Suggested Citation:"Chapter 4: Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2021. Investigating the Relationship of As-Constructed Asphalt Pavement Air Voids to Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/26219.
×
Page 136
Page 137
Suggested Citation:"Chapter 4: Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2021. Investigating the Relationship of As-Constructed Asphalt Pavement Air Voids to Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/26219.
×
Page 137

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

135 C H A P T E R 4 Conclusions and Suggested Research The research team identified and collected data on over 400 LTPP sections to form the dataset on which the analysis was based. The number and diversity of the sections was sufficient to examine the study objective and determine a general conclusion. On a broad basis, the research team concluded that lower as- constructed AV do have a positive influence on the performance of an asphalt pavement, but the influence is not consistent between types of pavements (new construction and rehabilitation) and measures of performance (rutting, fatigue cracking, thermal cracking, and ride). This conclusion is founded on the findings discussed above and the perspective of the diverse approaches used in the three analysis methods. A summary of the findings from all three analysis methods is given in Table 4-1. All three analysis methods concluded that the influence of as-constructed AV on pavement performance was mixed. Based on the validation results summarized in Table 4-2 comparing observed versus predicted plots and statistical analysis, it can be concluded that the models were predominantly developed with low to moderate traffic and caution is needed when applying the models to input parameter values outside the range of the LTPP dataset. The study team is reluctant to present the analysis results in this conclusion chapter as a summary because they represent an average of 200 sections with a wide deviation between measured and predicted values. The computed model predictions illustrate the potential influence of as-constructed AV, but should not be viewed as a national norm. Each agency needs to enter their own input values to examine the model predictions as directed in the next chapter, User Guide. Table 4-1. Summary of all analysis methods. Performance Measure Pavement Type Analysis Method 1 Analysis Method 2 Analysis Method 3 Rutting New 57% met expectation 21% contradict Approximate R2 = 0.31 Nominally Contradict Predicted R2 = 0.46 Nominally Met Rehabilitation 67% met expectation 0% contradict Approximate R2 = 0.16 Minimally Contradict Predicted R2 = 0.47 No Influence Fatigue Cracking New 82% met expectation 9% contradict Approximate R2 = 0.41 Significantly Met Predicted R2 = 0.62 Mixed Expectation Rehabilitation 40% met expectation 30% contradict Approximate R2 = 0.35 Nominally Met Predicted R2 = 0.46 Significantly Met Thermal Cracking New 42% met expectation 50% contradict Approximate R2 = 0.38 Nominally Met Predicted R2 = 0.36 No Influence Rehabilitation 50% met expectation 50% contradict Approximate R2 = 0.32 Nominally Met Predicted R2 = 0.30 Nominally Met Ride New 54% met expectation 38% contradict Approximate R2 = 0.24 Nominally met Predicted R2 = 0.19 Nominally Met

136 Rehabilitation 25% met expectation 42% contradict Approximate R2 = 0.21 Nominally Met Predicted R2 = 0.39 Nominally Met Table 4-2. Summary of the validation. Performance Measure Pavement Type Analysis Method 1 Analysis Method 2 Analysis Method 3 Rutting New PaveME predictions 50% agreement MnROAD No agreement NCAT No agreement MnROAD/NCAT (n=98) Rejected prediction F-value = 4.199 F-critical = 1.312 MnROAD/NCAT (n=27) Reject prediction F-value = 283.5 F-critical = 1.63 Rehab Insufficient validation data MnROAD/NCAT (n=63) Confirm prediction F-value = 1.355 F-critical = 1.395 Insufficient validation data Fatigue Cracking New PaveME predictions 50% agreement MnROAD 100% agreement NCAT 100% agreement MnROAD/NCAT (n=32) Confirm prediction F-value = 1.412 F-critical = 1.774 MnROAD/NCAT (n=4) Reject prediction F-value = 30.4 F-critical = 3.13 Rehab Insufficient validation data Insufficient validation data Insufficient validation data Thermal Cracking New Poor PaveME data MnROAD 50% agreement No NCAT data MnROAD (n=22) Confirm prediction F-value = 1.162 F-critical = 1.760 MnROAD (n=5) Reject prediction F-value = 38.5 F-critical = 2.80 Rehab Insufficient validation data Insufficient validation data Insufficient validation data Ride New Poor PaveME data MnROAD 50% agreement NCAT No agreement MnROAD/NCAT (n=73) Marginal reject prediction F-value = 1.811 F-critical = 1.362 MnROAD/NCAT (n=27) Reject prediction F-value = 713.1 F-critical = 1.63 Rehab Insufficient validation data MnROAD/NCAT (n=47) Marginal reject prediction F-value = 2.050 F-critical = 1.458 Insufficient validation data The research team developed a work plan for this 2018 study, executed that work plan, made several adjustments during the process, and noted mixed findings from the three analysis procedures. Looking back at the study with the perspective of the completed effort, the research team identified a number of items that could have been modified that may improve the understanding of the effect of as-constructed AV on asphalt pavement performance. The following list suggests topics for a future research approach.

137 • Eliminate extreme LTPP sections, such as under designed sections to accelerate fatigue and over designed sections to isolate the influence of climate. These sections do not represent typical pavement performance and create some abnormal skew in model development. • Replace some input variables with better parameters, such as using effective binder content in place of total binder content. The key to making this decision is the consistency and accuracy of the proposed variable. • Replace the ANN model input for traffic, average annual ESALs, with the time-dependent input variable, cumulative ESALs. Early attempts to develop the ANN models with cumulative traffic were unsuccessful, so the decision was made to use average annual ESALs. However, the influence of cumulative traffic over time is an important variable that warrants further consideration. • Identify other sources of data for model validation. Validation is an important component of model development and requires a complete, extensive dataset with all the model input and output features. The MnROAD and NCAT datasets were complete and extensive for some pavement performance criteria but represented extreme conditions on the boundaries of the national models. If an agency has an extensive pavement database, that data could be split to achieve both model development and validation. • Establish expected performance criteria for each pavement distress type. This 2018 national study applied one basic expectation (lower AV improves performance) for all four types of pavement performance and under all climate, traffic, and pavement structure conditions. A more thoughtful examination of climate, traffic, and structure subgroups could establish differences in performance expectation. For example, early rutting related to traffic is very different from thermal cracking related to age. • Use critical asphalt material input variables. There needs to be a balance between common, easily obtained material variables, such as gradation and binder content, and more critical material variables that may not be readily available, such as mixture permeability. While the focus of the study was the effect of as-constructed AV, the models need to properly predict pavement performance relative to all critical input variables. This is a complex issue that the PaveME model strived to accomplish. • Create an independent study to examine the effect of as-constructed AV on thermal cracking. The results of this 2018 study show that higher AV may improve thermal cracking performance of new pavements in low temperature climates. A study is needed to validate this finding. • Use the performance trend curves created by the quintile subgroups in Analysis Method 2 to establish the best performance equation for model development. This study selected specific math models to control model development for each type of performance. Examining the performance trends of each quintile group could improve the equation selection.

Next: Chapter 5: User Guide »
Investigating the Relationship of As-Constructed Asphalt Pavement Air Voids to Pavement Performance Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

Several controlled laboratory studies have shown that air voids (AV) can have a large effect on the performance of asphalt pavements. AVs that are either too high or too low can cause a reduction in pavement life.

The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document 299: Investigating the Relationship of As-Constructed Asphalt Pavement Air Voids to Pavement Performance determines the effect of in-place AVs on the performance of asphalt concrete (AC) pavements.

The document also has supplemental appendices that are available by request to Ed Harrigan. They include data sets for LTPP, Pavement ME Design Validation, MnROAD Validation, and NCAT Validation.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!