Time-Dependent Reliability Prediction of Bridge Member Based on MGPF and SHM Data

Abstract

In the long-term monitoring period, the structural health monitoring (SHM) system produces a huge amount of monitoring data. It becomes a very important thing that how to real-timely predict structural reliability indices from such huge number of monitored data. In this paper, To real-timely predict reliability of bridge members with real-time monitoring information, with the long-term mass monitored data of health monitoring system, the data-based dynamic model including observation equation and state equation is built, and then the mixed Gaussian particle filter (MGPF) is introduced. With particle filter method, Bayesian method and dynamic model, the posteriori distribution parameters of state variable and one-step forward prediction distribution parameters of monitored data are predicted. Through resampling technique, with MGPF, the prediction precision of dynamic model can generally increase. Based on the dynamic monitored data, the weights of resampled particles can be constantly updated. Therefore the problem of particle degradation is solved. Finally based on the real-time predicted distribution parameters, with the first order second moment (FOSM) method, the dynamic reliability of bridge member is predicted, and an actual example is provided to illustrate the application and feasibility of the proposed models and methods.