Identification of traffic-induced nodal excitations of truss bridges through heterogeneous data fusion

We propose a time domain Bayesian inference-based regularization approach for the identification of traffic-induced nodal excitations of truss bridges through heterogeneous data fusion. The measurements (e.g., accelerations, strains and displacements) are fused via a state space realization and rescaled for force identification. The unknown excitation time histories are inverted by solving an ill-posed least squares problem using the proposed Bayesian regularization approach. A smoothing operator is used in the regularization process for the purpose of de-noising. Uncertainties due to measurement noise are considered in the process of force identification. Finally, the proposed algorithm is numerically illustrated by a 27 bar truss bridge. Results demonstrate the robustness and effectiveness of the proposed algorithm for traffic-induced excitation identification with high accuracy.