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 Linear Regression Analysis Mix Design Neural Networks Pavement Design Quality Assurance Quality Control Rutting
• 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.
  
  
• Format:
  PDF
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:77b96b8dbe79f8151bc112ad066a7bff94a0bcaea0dc8eb2d28dcc67ba46c9b65f062a840db4a219c15d27f6b5031c9e680a28bad036e20816919451afe8b2ef
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/25455/dot_25455_DS1.pdf
• File Type:
  [PDF - 1.18 MB ]