System Reliability Analysis Of Bridge Structures Considering Correlation Of Failure Modes And Proof Modes

Bridge structures in service are subjected to long-term ambient environments and continuously increasing traffic demands, thereforce 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, etc., 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 updating and prediction of structural components and/or structures. In this dissertation, based on historical and monitored load effect data, by use of the Copula theory and Bayesian dynamic models(BDMs), a systematic study on reliability updating and prediction of bridge systems is carried out.The main research contents of this dissertation are as follows:(1) For the two-component systems and multiple-component systems with multiple failure modes, this paper presents a mixed copula model for time-independet reliability analysis of series systems, parallel systems, series-parallel systems and parallel-series systems. The mixed copula model is obtained with the chosen optimal Copula functions with the Bayesian method. Through a numerical example, it is illustrated that the calculated failure probability when considering the correlation between failure modes is larger than that without considering the correlation between failure modes. It is verified that the solved failure probability is conservative without considering the correlation between failure modes.(2) Based on the historical load effect data, the Bayesian method is used to update the distribution parameters of the resistance of bridge members. Based on the timely monitored load effect data, a mixed Bayesian dynamic model is proposed to predicte the distribution parameters of load effects of bridge emembers. Then, the first order second moment method(FOSM) is adopted to update and predict the time-dependent reliability of bridge members based on the updated resistance distribution parameters and the predicted load effect distribution parameters. And then, the reliability updating and prediction of bridge structural systems is analyed using the method put forward by Chapter 2. Two bridges in practice are selected as case studies. It is found that the obtained reliability indices with the updated prediction model based on monitored data, are larger than those without the updated prediction models. It is further verified that the predicted reliability indices without the updated prediction models are conservative.(3) Under the actions of the common random sources of the time-dependent input, the time-dependent nonlinear correlation will exist among the time-dependent output variables. The Bayesian dnamic models(BDMs) are introduced to predict the time-dependent output variables, and to model the time-dependent nonlinear correlation coefficients between them. The Gaussian Copula-BDMs are built based on the Gaussian Copula theory and the time-dependent correlation coefficients. The models can better and more feasibly forecast the future reliability of bridge structures. Through two actual application examples, it is illustrated that based on the Gaussian Copula-BDMs model, the predicted time-dependent reliability indices are smaller than those without considering the time-variant correlation between failure modes. Therefore, it is necessary to consider the correlation between failure modes for prediction of reliability indices of bridge systems.(4) Based on the everyday load effect data, time-dependent proof load effects of structural members are defined, and the corrensponding time-variant proof modes and time-variant performance functions are provided. And then, the load effects are predicted with the BDMs based on the timely monitored data. Further, the corresponding failure modes and performance functions are provided. Considering the approximate linear characteristics of the time-dependent failure modes and the time-dependent proof modes, the system reliability of bridge structures is updated and predicted based on the predicted failure modes, the predicted proof modes, and the correlation coefficients between them. Through two actual examples, it is illustrated that considering the correlation between failure mode s and the corrensponding proof modes, the predicted reliability indices are larger than the predicted values without considering the correlation between failure mode s and proof modes. It is further verified that it is reasonable and necessary for predicting time-dependent reliability indices to consider the correlation between failure modes and proof modes.