First Order Moving Force Identification Applied to Bridge Weigh-In-Motion

Bridge Weigh-In-Motion systems are based on the measurement of the deformation of a bridge and the use of these measurements to estimate the attributes of passing traffic loads. Despite many advantages, Bridge Weigh-In-Motion algorithms have often failed to predict axle weights accurately due to noise and vehicle and bridge dynamics. The algorithm in this paper uses Moving Force Identification theory and it applies first order Tikhonov regularization in conjunction with dynamic programming to predict the unknown traffic forces from simulated bridge strain measurements. An accurate finite element mathematical model that resembles the response of the bridge is needed to predict the applied forces. For this purpose, a calibration method based on the Cross-Entropy Optimization algorithm is used to adjust the mass and stiffness matrices of the finite element model. Once the model has been calibrated, the algorithm requires accurate velocity and axle spacing of the vehicle forces and the continuous strain record that they induce on the bridge. Sensitivity analyses are carried out to demonstrate the effect of errors in each of these required inputs. It is shown that the approach proposed herein has the potential to estimate static weights and the time history of the forcing function of each axle accurately.