At hundreds of weigh-in-motion (WIM) stations, state departments of transportation (DOTs) collect WIM data 24/7 to meet federal traffic reporting requirements. The North Carolina Department of Transportation (NCDOT) collects WIM data using procedures consistent with recommended industry practices to estimate static vehicle axle weights based on dynamic traffic measurements. Data errors and poor quality data are captured regardless of the technology used, and a quality control (QC) process is an important part of all WIM data systems. WIM data must undergo a series of sequential and well-defined QC procedures to ensure that the data meet the federal requirements and new standards for the Mechanistic Empirical Pavement Design Guide (MEPDG) process. This article documents the NCDOT WIM QC procedures. The results of the QC analysis provide reliable data sets for use in developing Levels 1, 2, and 3 traffic data inputs for the North Carolina MEPDG models.
This investigation assessed the sensitivity of Mechanistic–Empirical Pavement Design Guide (MEPDG) outcomes to normalized axle load spectra representing various loading conditions observed in the Specific Pavement Studies Transportation Pooled Fund Study of the Long-Term Pavement Performance program. The goal was to determine what vehicle classes and axle types with a wide range of axle loading conditions are likely to cause differences in pavement design outcomes when the MEPDG is used. Significant differences found in the MEPDG outcomes support the need for characterization of axle loading beyond a single default value for heavy trucks that dominate vehicle class distributions, especially for Class 9 trucks. The absence of differences for lightweight and under-represented trucks indicates that load spectra from various sites could be combined to develop a single default for some vehicle classes and axle types. The effect of bias in weigh-in-motion (WIM) axle weight measurements on the normalized axle load spectra estimates and the associated MEPDG outcomes was also investigated. It was found that drift in WIM system calibration leading to a more than 5% bias in mean error between true and WIM-measured axle weight could lead to significant differences in MEPDG design outcomes. These results were used to develop recommendations for creating axle loading defaults for the MEPDG.
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