Research on damage identification of simply supported bridge based on effect size method for vehicle-bridge coupled vibration

桥(图论) 结构工程 振动 鉴定(生物学) 计算机科学 声学 工程类 物理 医学 植物 内科学 生物
作者
Yihang Guo,Jiangfei Wang,Yanni Wu,Jianguo Xu,Liang Huang,Qi Zhou,Lei Kou
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:36 (1): 016152-016152 被引量:3
标识
DOI:10.1088/1361-6501/ad9042
摘要

Abstract This paper presents a novel bridge damage identification method employing Cohen’s d as an effect size indicator, predicated on the detection of bridge damage through the coupled vibration between a vehicle and the bridge. By analysing the dynamic response of the bridge as a vehicle passes over, this method effectively extracts the modal parameters of the bridge and facilitates the identification of bridge damage. Numerical models of the bridge under various damage conditions, including no damage, mid-span damage, and damage at the 1/4 and 3/4 span locations, have been constructed to substantiate the efficacy and precision of the effect size as a damage indicator. Furthermore, to address the challenge of accurately identifying damage at boundaries, which is often confounded by boundary effects, a boundary subdivision method has been defined for the detection of boundary damage. Through the implementation of a real-bridge test based on indirect measurement technology, the self-vibration frequency of the bridge was successfully extracted. This empirical data was then compared and analysed against results from ANSYS numerical simulations, thereby validating the practicality and accuracy of the proposed method. In the final analysis, the influence of random traffic flow on the bridge damage identification results was examined. The findings indicate that the vibrations in simply supported beam bridges are intensified due to the impact of random traffic flow, which aids in enhancing the accuracy of damage identification. The introduction of this method provides a new quantitative tool for bridge health monitoring, enabling the rapid and accurate identification of bridge damage without interrupting traffic. It holds significant value for engineering applications in bridge maintenance and safety assessment.
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