Investigation of the Asphalt Pavement Analyzer (APA) testing program in Nebraska.

Details

• Creators:
  Kim, Yong-Rak ; Hong, Minki
• Corporate Creators:
  University of Nebraska--Lincoln. Dept. of Civil Engineering
• Subject/TRT Terms:
  • Hot mix asphalt
  • Rutting
  • Pavement design
  • Mix design
  • Quality assurance
  • Quality control
  • Neural networks
  • Linear regression analysis
• Publication/ Report Number:
  MPM-16
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Nebraska Transportation Center
• Abstract:
  The asphalt pavement analyzer (APA) has been widely used to evaluate hot-mix asphalt (HMA) rutting potential in mix ; design and quality control-quality assurance (QC-QA) applications, because the APA testing and its data analyses are ; relatively simple, rapid, and easy. However, as demonstrated in many studies and also experienced by the state of ; Nebraska, APA testing is in question due to its high testing variability and a lack of sufficient correlation with actual filed ; performance. The primary objective of this research was to find critical materials and/or mixture design factors affecting ; APA test results so as to eventually improve the current APA testing program in Nebraska. In addition to that, ; development of models to predict APA rut performance with given properties of HMA mixture ingredients and mixture ; design characteristics were also attempted. To find variables affecting APA rut results and the extent of these variables, ; SP-4 mixture data from Nebraska and HMA mixture data from Kentucky were statistically analyzed using the multiple ; linear regression method considering six factors (binder PG, aggregate gradation, nominal maximum aggregate size, ; aggregate angularity, air voids in mixture, and asphalt content in mixture) as probable candidates for significantly ; affecting APA rut results. For a detailed characterization of gradation effects, three indicators (gradation density, fineness ; modulus, and restricted zone) were considered, and each of them was used for each statistical analysis. Results from ; analyses demonstrated that the binder PG was the only variable that always shows significant impact on APA rut results, ; which is in good agreement with other studies. Predicting models developed through the results of multiple linear ; regression analysis and the artificial neural network technique presented a relatively low level of model adequacy which ; can be observed by the coefficients of determination and cross-plots between predicted APA rut values and the measured ; APA rut data. More data would be helpful to confirm the findings from this research and also to develop a better ; prediction model.
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• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/25455/dot_25455_DS1.pdf
• File Type:
  [PDF - 1.18 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:77b96b8dbe79f8151bc112ad066a7bff94a0bcaea0dc8eb2d28dcc67ba46c9b65f062a840db4a219c15d27f6b5031c9e680a28bad036e20816919451afe8b2ef