Gaussian copula-Bayesian dynamic linear model-based time-dependent reliability prediction of bridge structures considering nonlinear correlation between failure modes

Abstract

Bridge structures in service are subjected to long-term ambient environments and continuously increasing traffic demands; therefore, the physical quantities of the existing bridge structures are subjected to changes in both time and space. Through health monitoring for bridges, the data of the load effects of bridge structures, including strain, stress, deflection, and so on, of the specified structural components or structures, can be obtained. The novel monitoring systems installed in bridge structures contain sensors providing a large amount of monitored data. Proper processing of the continuously provided monitored data is one of the main difficulties in the field of structural health monitoring for time-dependent reliability prediction of structural components and/or structures. Under the actions of the common random sources of the time-dependent input load data, the time-dependent nonlinear correlation will exist among the time-dependent output load effect data. The Bayesian dynamic linear models are introduced to predict the time-dependent output variables and model the time-dependent nonlinear correlation coefficients between them. Then, the Gaussian copula-Bayesian dynamic linear models are built based on the Gaussian copula theory and the time-dependent correlation coefficients. The models can better and more feasibly predict the future reliability of bridge structures. Finally, an actual application example is provided to illustrate the feasibility and application of the built Gaussian copula-Bayesian dynamic linear models for structural reliability prediction.