Verification of the Axle Load Spectra Databases Using Stationary and Portable Weight-in-Motion Devices in Overload Corridors of East Texas

Abstract

Axle Load Spectra (ALS) data collected from the Portable Weight-in-Motion (P-WIM) devices, provides the primary Mechanistic-Empirical (ME) traffic data input for optimal and accurate pavement design and analysis. Reliable readings from the P-WIM devices are the key factors that contribute to the accuracy of the analysis results. Therefore, this study was aimed to accurately assess the reliability and quality of the traffic data directly derived from the field data collection efforts. To accomplish this objective, the authors initially deployed P-WIM devices to US281 highway as a representative site in Texas overload corridors to collect the traffic data. The results were synthesized to compile the site-specific axle load spectra database, comprising of traffic information on the axle weights, vehicle classifications, and axle configurations. Subsequently, to assess the reliability of the collected data, P-WIM achieved traffic data were contrasted with those captured by the stationary WIM located at the vicinity of the evaluated site, using the available databases. Comparative analysis results indicated that traffic characterizations using the two WIM systems led to comparable outcomes, validating the accuracy and reliability of the P-WIM data measurements in the field. Additionally, as a practical means to investigate the quality of the recorded data, the longevity of the P-WIM piezo-sensors in several sites with different traffic patterns was investigated. Hence, the deterioration of the calibration factors over the operational life of the installed piezo-electric sensors in the field was analyzed. The post-processed results revealed that the piezo-electric sensors sustained substantial damage after nearly 37 days of operation in the field. Consequently, proper quantification of the ALS should include cross-validation assessments, as well as continuous evaluations of the calibration factors throughout the P-WIM data collection process to achieve good-quality, accurate, and reliable traffic data.