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Bridge finite element model updating using stochastic vehicle-induced static response monitoring data
Abstract Finite element model updating (FEMU) is a frequent method for minimizing the differences between actual and predicted bridge structural behavior. However, static model updating for long-span bridges usually requires load tests to obtain the response under known static loads, which is costly due to long traffic interruptions. To offset this disadvantage, a static FEMU method based on long-term monitoring data is proposed to update the model in real-time by taking stochastic traffic loads with certain statistical characteristics as known static loads. First, the theoretical relationship between stochastic traffic flow statistical characteristics, static response statistical characteristics, and structural stiffness parameters is established. An objective function based on the probability characteristics of the long-term vehicle-induced response is constructed. Then, the FEMU process based on monitoring data is introduced, including the acquisition of the finite element model vehicle-induced response, the extraction of the actual vehicle-induced response based on the monitoring data, the selection of model updating parameters, and the application of the global-local parameter updating optimization method. Finally, the effectiveness of the proposed method is verified by applying numerical examples and real bridge examples to a long-span suspension bridge. It can be concluded that the proposed method can achieve FEMU based on long-term monitoring data, avoiding the inconvenience and large cost caused by load tests. The accuracy and efficiency of the model updating can meet the engineering requirements well through comparison with the result of the traditional load test method.
Highlights This paper presents a static finite element model updating method based on the statistical characteristics of long-term monitoring data during normal operation, avoiding the inconvenience and large cost caused by the previous model updating method based on the load test. The proposed method takes stochastic traffic load with known probability characteristics as known static load, and can realize model updating well by reducing the error of the actual response probability characteristics and finite element model prediction response probability characteristics. The probabilistic characteristics of long-term vehicle-induced static response are related to structural stiffness parameters and probabilistic characteristics of stochastic traffic flow. When the probabilistic characteristics of stochastic traffic flow are the same, the static response probabilistic characteristics are related to only the structural stiffness parameters.
Bridge finite element model updating using stochastic vehicle-induced static response monitoring data
Abstract Finite element model updating (FEMU) is a frequent method for minimizing the differences between actual and predicted bridge structural behavior. However, static model updating for long-span bridges usually requires load tests to obtain the response under known static loads, which is costly due to long traffic interruptions. To offset this disadvantage, a static FEMU method based on long-term monitoring data is proposed to update the model in real-time by taking stochastic traffic loads with certain statistical characteristics as known static loads. First, the theoretical relationship between stochastic traffic flow statistical characteristics, static response statistical characteristics, and structural stiffness parameters is established. An objective function based on the probability characteristics of the long-term vehicle-induced response is constructed. Then, the FEMU process based on monitoring data is introduced, including the acquisition of the finite element model vehicle-induced response, the extraction of the actual vehicle-induced response based on the monitoring data, the selection of model updating parameters, and the application of the global-local parameter updating optimization method. Finally, the effectiveness of the proposed method is verified by applying numerical examples and real bridge examples to a long-span suspension bridge. It can be concluded that the proposed method can achieve FEMU based on long-term monitoring data, avoiding the inconvenience and large cost caused by load tests. The accuracy and efficiency of the model updating can meet the engineering requirements well through comparison with the result of the traditional load test method.
Highlights This paper presents a static finite element model updating method based on the statistical characteristics of long-term monitoring data during normal operation, avoiding the inconvenience and large cost caused by the previous model updating method based on the load test. The proposed method takes stochastic traffic load with known probability characteristics as known static load, and can realize model updating well by reducing the error of the actual response probability characteristics and finite element model prediction response probability characteristics. The probabilistic characteristics of long-term vehicle-induced static response are related to structural stiffness parameters and probabilistic characteristics of stochastic traffic flow. When the probabilistic characteristics of stochastic traffic flow are the same, the static response probabilistic characteristics are related to only the structural stiffness parameters.
Bridge finite element model updating using stochastic vehicle-induced static response monitoring data
Guan, Ze-Xin (author) / Yang, Dong-Hui (author) / Yi, Ting-Hua (author) / Li, Wen-Jie (author) / Li, Chong (author)
Engineering Structures ; 301
2023-12-02
Article (Journal)
Electronic Resource
English
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