FULLY-NEURAL APPROACH TO VEHICLE WEIGHING AND STRAIN PREDICTION ON BRIDGES USING WIRELESS ACCELEROMETERS

Bridge weigh-in-motion (BWIM) is a technique of estimating vehicle loads on bridges and can be used to assess a bridge's structural fatigue and therefore its life. BWIM can be realized by analyzing the bridge deflection in terms of its response to moving axle loads. To obtain accurate load estimates, current BWIM systems require strain sensors, whose (re-) installation costs have limited their application. In this paper, we propose a new BWIM approach based on a deep neural network using accelerometers, which are easier to install than strain sensors, thus helping the advancement of low-cost BWIM systems. By learning the bridge dynamism, our model estimates axle loads successfully from the noisy acceleration signals sampled on a real bridge in service.

Automated summary

This paper introduces a new approach for Bridge Weigh-In-Motion (BWIM) utilizing deep neural networks and accelerometers to estimate axle loads on bridges more affordably compared to traditional strain sensor systems. By analyzing acceleration signals, this model successfully estimates axle loads in a real bridge setting.