Abstract

AbstractThe vehicle running stability on the bridge under a strong crosswind has become a hot topic with increasing concern in recent years. The traditional analysis method of wind-vehicle-bridge system is complicated and time-consuming, which has not fully utilized a large amount of valuable in-situ monitoring data and cannot meet the requirement of timely decision. Besides, the previous analysis method has not been validated through the experiment. This study presents a combined analysis scheme integrating the FEM (Finite Element Method) and MBS (Multiple Body Simulation) to be more convenient and comprehensive to analyze the Vehicle-Bridge-Interaction (VBI) system. A new double-dummy coupling method is proposed to simulate the vehicle-bridge connection to solve the vehicles' responses accurately and efficiently. Other influence factors like tire model, wind force, and road roughness are taken into account as well. Furthermore, to consider the wind effect on the VBI system and avoid the complicated fluid–structure interaction, the in-situ monitoring data could be fully applied in this scheme. A monitoring-based approach is proposed to hybrid the monitoring data and MBS-FEM model to obtain the vehicle response. To validate the proposed analysis scheme, a vehicle passing through Chongqi Bridge under the strong wind is an experiment case study. Firstly, regarding to the limited number of measurement sensors on the bridge, the bridge monitoring response is expanded through an improved perturbed force method combined with genetic algorithm method, and the wind speed is simulated with linear filtering method. Next the expanded monitoring data is combined with MBS-FEM model to comprehensively consider the wind effect on the vehicle and bridge. Finally, the vehicle responses from the MBS-FEM model are compared with experimental data, showing the accuracy and good reliability of this method. This monitoring-based MBS-FEM method also is expected be used for stability analysis of vehicles running on bridges under the strong wind.KeywordsVehicle-bridge-interactionFEMMBSMonitoring data

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