Weigh-in-Motion Data Quality Assurance Based on 3-S2 Steering Axle Load Analysis
Ott W, A. T. Papagiannakis
Abstract
Ott W, A. T. Papagiannakis
Abstract
An innovative method is offered for conducting the quality assurance of weigh-in-motion (WIM) data by analyzing the variation in the measurements of five-axle semitrailer (3-S2) truck steering-axle loads. Confidence-interval limits for those measurements are established on the basis of their historic mean static loads adjusted for the effect of air resistance and their combined variation from two sources, the variation within a fleet of 3-S2 trucks and the variation due to axle dynamics at a WIM site. The first variation was determined by analyzing historic 3-S2 load data obtained by FHWA using static scales. The second variation was established through dynamic vehicle simulations of a “typical” 3-S2 truck using vehicle model VESYM and the roughness profile at a particular WIM site. The method was tested with data from four WIM systems in the state of Washington, two bending-plate systems and two piezoelectric systems. The proposed method resulted in clear indications of the performance of the scale versus time by the number of confidence interval violations observed.
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An innovative method is offered for conducting the quality assurance of weigh-in-motion (WIM) data by analyzing the variation in the measurements of five-axle semitrailer (3-S2) truck steering-axle loads. Confidence-interval limits for those measurements are established on the basis of their historic mean static loads adjusted for the effect of air resistance and their combined variation from two sources, the variation within a fleet of 3-S2 trucks and the variation due to axle dynamics at a WIM site. The first variation was determined by analyzing historic 3-S2 load data obtained by FHWA using static scales. The second variation was established through dynamic vehicle simulations of a “typical” 3-S2 truck using vehicle model VESYM and the roughness profile at a particular WIM site. The method was tested with data from four WIM systems in the state of Washington, two bending-plate systems and two piezoelectric systems. The proposed method resulted in clear indications of the performance of the scale versus time by the number of confidence interval violations observed.
Key concepts: Weigh in motion, Truck, Axle, Axle load, Quality assurance, Engineering, Structural engineering, Automotive engineering