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.

中文翻译：

---

基于多元贝叶斯动态高斯copula模型和SHM极限应力数据的钢箱梁动态可靠度预测

本文提出了一种考虑多个控制监测点失效模式性能函数之间时变非线性相关性的现有钢箱梁动态可靠性预测方法。首先，建立了考虑多个变量之间非线性相关性的多元贝叶斯动态线性模型（MBDLM），以预测不同控制监控点的极限应力。其次，基于MBDLM的预测协方差矩阵，可以准确地预测任意两个性能函数之间的动态相关系数。最后，结合MBDLM和高斯copula技术，提出了多元贝叶斯动态高斯copula模型，以预测钢箱梁的动态可靠性。通过实际桥梁的极限应力监测数据，说明了该方法的可行性和应用价值。结果表明，考虑多个控制监测点故障模式的时变非线性相关性，预测桥梁的动态可靠性要大于不考虑时变非线性相关性的预测动态可靠性。结果表明，在不考虑多个控制监控点故障模式动态非线性相关性的情况下，预测结果较为保守。结果表明，考虑多个控制监测点故障模式的时变非线性相关性，预测桥梁的动态可靠性要大于不考虑时变非线性相关性的预测动态可靠性。结果表明，在不考虑多个控制监控点故障模式动态非线性相关性的情况下，预测结果较为保守。结果表明，考虑多个控制监测点故障模式的时变非线性相关性，预测桥梁的动态可靠性要大于不考虑时变非线性相关性的预测动态可靠性。结果表明，在不考虑多个控制监控点故障模式动态非线性相关性的情况下，预测结果较为保守。