No abstract

This article presents the dynamic reliability prediction method of the existing steel box girder considering the time-variant nonlinear correlations among the performance functions for the failure modes at the multiple control monitoring points. Firstly, the multivariate Bayesian dynamic linear model (MBDLM) considering the nonlinear correlations among the multiple variables is built to predict the extreme stresses at the different control monitoring points; secondly, based on the predicted covariance matrix of MBDLM, the dynamic correlation coefficients between any two performance functions can be accurately predicted; and finally, multivariate Bayesian dynamic Gaussian copula model through combining MBDLM with Gaussian copula technique is proposed to predict the dynamic reliability of the steel box girder, and the monitoring extreme stress data of an actual bridge are provided to illustrate the feasibility and application of the proposed method. The results show that predicted dynamic reliability of the bridge girder with considering the time-variant nonlinear correlation of failure modes at the multiple control monitoring points is bigger than that without considering the time-dependent nonlinear correlation. It is illustrated that the predicted results without considering the dynamic nonlinear correlation of failure modes at the multiple control monitoring points are more conservative. K E Y W O R D S dynamic reliability prediction, first-order second moment (FOSM) method, multivariate Bayesian dynamic Gaussian copula model, steel box girder, time-variant nonlinear correlations

Structural health monitoring systems produce a large number of monitored data in the long-term service periods. To properly predict the dynamic reliability of bridge system based on these structural health monitoring data, the paper is to propose (a) Bayesian Dynamic Gaussian Copula Model obtained with Bayesian dynamic linear models and Gaussian Copula function for modeling the time-dependent nonlinear correlation coefficients between monitored variables; (b) time-variant reliability prediction approach of bridge system based on Bayesian Dynamic Gaussian Copula Model; and (c) an actual bridge is provided to illustrate the feasibility and application of the proposed models and methods.