Dynamic real-time reliability prediction of bridge structures based on Copula-BHDLM and measured stress data

This study presents a novel dynamic real-time reliability prediction method by considering the time-varying correlation among the data at multiple measurement points. The Bayesian Hilbert dynamic linear model (BHDLM) is developed to predict the stress response, and the dynamic real-time reliability is then predicted by using the Pair-Copula theory. To illustrate the feasibility of the proposed method, the dynamic real-time reliability of a steel-concrete composite girder bridge based on the simulated stress data due to a single vehicle with various weights and random traffic flow load is predicted. Finally, the dynamic reliability of a real bridge based on the monitoring stress data is predicted. The results show that the proposed BHDLM is effective for predicting coupled stresses, and the dynamic real-time reliability can be predicted. In addition, the predicted dynamic reliability of the real bridge demonstrates that the influence of the data correlation should be considered.

Automated summary

This study presents a novel dynamic real-time reliability prediction method by considering the time-varying correlation among the data at multiple measurement points. The Bayesian Hilbert dynamic linear model (BHDLM) is developed to predict the stress response, and the dynamic real-time reliability is then predicted by using the Pair-Copula theory. The feasibility of the proposed method is illustrated by predicting the dynamic real-time reliability of a steel-concrete composite girder bridge based on simulated stress data and a real bridge based on monitoring stress data.