Effect of Axle Load Measurement Errors on Pavement Performance and Design Reliability

Abstract

In traffic characterization, axle load spectra (ALS) are one of the most critical inputs in the new Mechanistic–Empirical Pavement Design Guide (MEPDG). ALS have a significant impact on the predicted pavement performance. At the design stage, it is typically assumed that ALS as measured by weigh-in-motion (WIM) systems have adequate data quality and accuracy. In fact, the quality of WIM-based data has inherent uncertainties because of inaccuracy and systematic bias. While WIM data accuracy depends on the sensor technology, calibration errors and drift over time may introduce a systematic bias. Several studies have investigated the impact of traffic data collection technologies, data coverage, accuracy, and calibration errors on pavement loading and performance prediction. However, these studies were limited to a few distress measures and did not address design reliability aspects as considered in the MEPDG. This study investigated the impact of probable WIM errors on the ALS and quantified the effects of these errors on the performance of both flexible and rigid pavements. Furthermore, the impact of uncertainties in ALS on design reliabilities is discussed in this paper. Although most findings reinforce existing concepts, the study provides a systematic overview of WIM data accuracy and calibration requirements, and the effect of associated uncertainties in the pavement design process. The results show that cracking in both flexible and rigid pavements is the distress most affected by ALS variations, while rutting in flexible pavements is moderately affected.