Impact of Systematic Axle Load Measurement Error on Pavement Design Using Mechanistic-Empirical Pavement Design Guide

Abstract

In traffic characterization, axle load spectra (ALS) are one of the most critical inputs in the new Mechanistic-Empirical Pavement Design Guide (MEPDG). Axle load spectra have a significant effect on predicted pavement performance and, thus, the design life. Typically, axle load spectra as measured by weigh-in-motion (WIM) systems are assumed to have adequate data quality and accuracy. In fact, the quality of WIM-based data has inherent uncertainties attributable to inaccuracy and systematic bias. Whereas WIM data accuracy depends on the sensor technology, calibration errors and drift over time may introduce a systematic bias. This technical note investigates the effect of axle load measurement bias on pavement design for flexible and rigid pavements. The results show that negative bias in axle load measurements significantly affects cracking performance for both pavement types. The bias is more critical for rigid pavements with thinner slabs. Therefore, a measurement bias limit of less than