Dynamic Reliability Prediction of Bridges Based on Gaussian Particle Filter with Discount Factors

Abstract:

Bridge health monitoring (BHM) system produces a huge amount of monitored data in the longterm service periods, the proper handling of which for dynamically predicting the structural reliability is one of the main difficulties in the BHM field. To reasonably predict timevariant reliability of inservice bridges, in this paper, the linear dynamic models (monitoring equation and state equation) with optimum discount factors are built based on the longterm everyday monitored extreme stress data of BHM system. Then, the onestep forward prediction distribution parameters of monitored extreme stress and the posteriori distribution parameters of state variable are respectively predicted by using the Gaussian particle filter prediction algorithm with discount factors. Finally, the dynamic reliability indices of bridge are predicted using the first order second moment (FOSM) method, and the monitored data of an actual bridge is provided to illustrate the application and feasibility of the proposed method, which can provide the theoretical foundation for preventive maintenance decision of the actual bridge.