Clustering Vehicle Class Distribution and Axle Load Spectra for Mechanistic-Empirical Predicting Pavement Performance
Publication: Journal of Transportation Engineering
Volume 142, Issue 11
Abstract
Past studies have determined the effects of the pavement mechanistic-empirical (ME) default (Level 3) values of vehicle class distribution (VCD) and axle load spectra (ALS) on pavement performance. However, it is still not clear how the clustered VCD and ALS affect the ME predicted pavement performance. In this study, traffic data from 10 weigh-in-motion (WIM) stations were gathered and analyzed to develop the VCD and ALS values using arithmetic average and clustering methods (Level 2). Next, using Level 2, Level 3, and site-specific (Level 1) inputs of VCD and ALS, the pavement ME predicted performance was determined. The results show that the predicted performance by the cluster (Level 2) data are very close to those of the site-specific data (Level 1). Performance generated by the ME default values (Level 3) are significantly different from those generated by the site-specific or cluster values. When comparing the performance of the ME design default (Level 3) with those of the statewide average data, the ME design default VCD produces fewer errors than the ALS. This study recommends using clustered or site-specific WIM data instead of ME default or statewide average value.
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Acknowledgments
This study is funded by the New Mexico Department of Transportation (NMDOT). The authors would like to express their sincere gratitude and appreciation to the Project Technical Panel Members, Project Advocate (Jeff Mann), and the Project Manager (Virgil Valdez) of NMDOT.
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Published In
Journal of Transportation Engineering
Volume 142 • Issue 11 • November 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Dec 13, 2015
Accepted: Mar 17, 2016
Published online: Jul 7, 2016
Published in print: Nov 1, 2016
Discussion open until: Dec 7, 2016
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