Dynamic reliability prediction of bridge member based on Bayesian dynamic nonlinear model and monitored data

Abstract

Bridge monitoring systems provide a huge number of stress data used for reliability prediction. In this article, the dynamic measure of structural stress over time is considered as a time series, and considering the limitation of the existing Bayesian dynamic linear models only applied for short-term performance prediction, Bayesian dynamic nonlinear models are introduced. With the monitored stress data, the quadratic function is used to build the Bayesian dynamic nonlinear model. And two methods are proposed to handle with the built Bayesian dynamic nonlinear model and the corresponding probability recursion processes. One method is to transform the built Bayesian dynamic nonlinear model into Bayesian dynamic linear model with Taylor series expansion technique; then the corresponding probability recursion processes are completed based on the transformed Bayesian dynamic linear model. The other one is to directly handle with the built Bayesian dynamic nonlinear model and the corresponding probability recursion processes with Markov chain Monte Carlo simulation method. Based on the predicted stress information (means and variances) of the above two methods, first-order second moment method is adopted to predict the structural reliability indices. Finally, an actual engineering is provided to illustrate the application and feasibility of the above two methods.