Quality Control for Weigh-in-Motion Data Incorporating Threshold Values and Rational Procedures

One of the major improvements with using the Mechanistic-Empirical Pavement Design Guide (MEPDG) occurs in its characterization of traffic. Instead of converting all Class 4 to Class 13 truck axles to 18,000 lb equivalent single axles (ESALs), the MEPDG simulates every truck axle, and the associated stresses and strains imposed on the pavement structure, from a wide range of axle load spectra (ALS). For this reason, the MEPDG needs traffic inputs in more detail than previous empirical pavement design methods, and thus, a higher requirement of weigh-in-motion (WIM) data quality. This paper presents a new and objective approach to quality control (QC) of WIM data to ensure data quality for pavement design purposes. Instead of using subjective visual comparisons of gross vehicle weight (GVW) distributions, this research implements a peak-range check, peak-shift check and correlation analysis to quantify the ALS comparison process of rational checks. A number-of-axles check that calculates the average number of axles per vehicle class is also introduced herein. The entire QC procedure has been applied to 12 WIM stations in Alabama. As a result, 30.6% of data were filtered out, and data from one entire WIM station were removed. Therefore, QC of WIM data is strongly recommended, regardless of the extent of WIM system calibration.