Dynamic Calibrating of Multiscale Bridge Model Using Long-Term Stochastic Vehicle-Induced Responses

Abstract

The traditional multiscale model static updating method for long-span bridges requires load tests to obtain the correspondence between load and response, which leads to prolonged traffic interruption, with poor timeliness and low efficiency. Therefore, an efficient multiscale model dynamic calibrating framework based on stochastic vehicle-induced responses is proposed in this paper. The multiscale model is calibrated by monitoring data, and the dynamic calibrating efficiency is improved through the substructure–refined model combination modeling. First, the relationship between the structure and its corresponding response statistical characteristics is derived under stochastic traffic loads, and a statistical-based calibrating objective function of the multiscale model is established. Second, the framework for efficient multiscale model dynamic calibrating based on long-term monitoring data is presented, including efficient multiscale model establishment and dynamic calibrating based on stochastic vehicle-induced responses. Finally, the effectiveness of the proposed method is verified by its application to a long-span steel box girder suspension bridge. Comparison with the traditional load test method demonstrates that the proposed method effectively achieves multiscale model dynamic calibrating based on monitoring data during bridge operation, improving calibrating efficiency while ensuring multiscale modeling accuracy.