Establishment of statewide axle load spectra data using cluster analysis Academic Article uri icon


  • 2015, Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg. The role of traffic data in the Mechanistic-Empirical (M-E) pavement design is significantly crucial in assessment of pavement performance throughout the pavement design life. Providing a well-qualified traffic data for the M-E pavement design requires a huge effort with respect to collecting and analyzing the data. The Texas Department of Transportation (TxDOT) manages 30 Weighin-Motion (WIM) stations and provided classification and weight data used for this study. Evaluating the classification data first, vehicle classification distribution along with the percentage of trucks, average annual daily truck traffic, and monthly adjustment factor were established. Processing axle load data provided the number of axles per truck and axle load spectra for each of the traditional 13 vehicle classes. To develop statewide axle load spectra data, the cluster analysis was conducted using vehicle classification distribution and the Class 9 tandem axle load spectra data to provide traffic input data for the Texas M-E flexible pavement design program where the load spectra data are not available due to the absence of WIM station. As a result, six clusters for both variables were identified and a guideline was successfully established to use those clusters to generate axle load spectra inputs for a given set of truck traffic classification data.

published proceedings

  • KSCE Journal of Civil Engineering

author list (cited authors)

  • Oh, J., Walubita, L. F., & Leidy, J.

citation count

  • 8

complete list of authors

  • Oh, Jeongho||Walubita, Lubinda F||Leidy, Joe

publication date

  • November 2015