Enhanced Bridge Weigh-in-Motion System Using Hybrid Strain–Acceleration Sensor Data

Abstract

In this study, a novel acceleration-based vehicle identification method is employed within a hybrid bridge weigh-in-motion (BWIM) system in which the traditional strain-based BWIM system is augmented with an array of accelerometers. The implementation of such a system is discussed through a full-scale case study arterial highway bridge in the province of New Brunswick, Canada. The accuracy of the proposed vehicle identification method was studied in detail using an extensive set of field study data. To achieve this, a systematic evaluation of existing methods for velocity estimation and axle identification was conducted, evaluating the effects of vehicle direction, lane position, vehicle velocity, and vehicle configuration. The methods were compared based on the sensor signal characteristics, the velocity estimation techniques, axles detection methods, and the effects on gross vehicle weight (GVW) calculation. From this study, it was found that the proposed hybrid system resulted in more accurate velocity estimation, axle identification, and ultimately better GVW estimation.