A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Clustering Vehicle Class Distribution and Axle Load Spectra for Mechanistic-Empirical Predicting Pavement Performance
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.
Clustering Vehicle Class Distribution and Axle Load Spectra for Mechanistic-Empirical Predicting Pavement Performance
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.
Clustering Vehicle Class Distribution and Axle Load Spectra for Mechanistic-Empirical Predicting Pavement Performance
Hasan, Md Amanul (author) / Islam, Md Rashadul (author) / Tarefder, Rafiqul A. (author)
2016-07-07
Article (Journal)
Electronic Resource
Unknown
Axle Load Distribution for Mechanistic-Empirical Pavement Design
| Online Contents | 2007
| Taylor & Francis Verlag | 2015