Axle Load Distribution for Mechanistic–Empirical Pavement Design in North Carolina

Abstract

A multidimensional clustering approach to generate regional average truck axle load distribution factors (ALDFs) for North Carolina is presented. The results support the Mechanistic–Empirical Pavement Design Guide (MEPDG). Weigh-in-motion data collected on North Carolina roadways are used in the analysis. A multidimensional clustering analysis based on ALDF data develops representative clusters for different highway functional classifications. Findings show that ALDF clusters have distinct characteristics for primary roads, secondary roads, collectors, and local roads. An easy-to-use decision tree based on available traffic parameters and local knowledge helps the pavement designer select the proper ALDF input. Specific contributions include a multidimensional clustering analysis that is guided by MEPDG damage-based analysis, well-defined ALDF clusters that represent specific traffic patterns in North Carolina, and a decision tree characterized by its simplicity to help pavement designers select ALDF inputs.