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Seismic Damage Identification for Bridges Based on Transmissibility Function and Support Vector Machine

JIANAN MI, YIXIAO ZHANG, WEIFENG LIU, LIJUN LIU

Abstract


Vibration-based damage identification and structural health monitoring have been developed in recent years. However, it is still a challenge to effectively extract the information of structural damage from a large amount of monitoring data and to realize the rapid detecting damages and assessing structural conditions of bridge structures under seismic excitation. To address this problem, transmissibility function (TF) is used in this paper, and based on the seismic monitoring data of bridge structures, an intelligent algorithm integrating transmissibility function and support vector machine (SVM) is developed for multi-level/scale structural damage identification under seismic excitation. Firstly, the TF is calculated with the measured structural vibration responses. Then, the indicator takes the sum of transmissibility along the specific frequency range is used as input for SVM. Finally, the classification algorithm of support vector machine is employed to structural damage alarming under earthquake. The proposed method could eliminate the influence of external excitations because transmissibility function only depends on the measured seismic responses of structures and the information of external excitations is not required. Further, the proposed method is sensitive to the structural damage. To illustrate the performances and feasibility of the proposed algorithm, the damage identification of numerical simulation under seismic excitation is studied.


DOI
10.12783/shm2019/32399

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