Bridge weigh-in-motion using augmented Kalman filter and model updating

Most of the bridge weigh-in-motion (B-WIM) systems in use adopt the static approach. For these systems, dynamic components of the bridge response constitute a significant cause of the prediction discrepancy. This study presents the framework of B-WIM leveraging the augmented Kalman filter, in which the bridge dynamic responses and the vehicle weights are estimated simultaneously. This approach considers the uncertainties from the modeling to the experimental measurement in a stochastic way. Structural identification is embedded to calibrate the digital model of the tested structure for a reliable mathematical representation. Parameter tuning of the Kalman filter method using optimization is also established. The effectiveness of the proposed method is then tested with a scaled model. The results show that the method can successfully estimate the weight of the vehicle with reasonable accuracy.