KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Damage Detection for Bridges Using Ambient Vibration Data

상시진동 데이테를 이용한 교량의 손상추정기법

Lee, Jong-Jae;Lee, Jong-Won;Yun, Chung-Bang
이종재;이종원;윤정방

  • Published : 2005.03.31

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Abstract

In this study, the neural networks-based damage detection of bridge structures is presented using ambient vibration data caused by traffic loadings. The general procedure consists of construction of a baseline finite element (FE) model at the initial stage of the bridge operation, ambient vibration test, identification of the modal parameters, and assessment of the damage locations and severities based on the changes of modal properties. In this study, it is assumed that there exists unknown error in the baseline FE model, then, modal parameters less sensitive to the modeling errors are employed as the input to the neural networks to reduce the effect of the discrepancies between the modes calculated from the initial FE model and the measured ones. In laboratory test, vehicle running test was performed on a simply supported bridge model using model vehicles to simulate traffic loads. In field test on a Hannam Grand Bridge, ambient vibration tests were carried out using the traffic loads on the adjacent new bridge and the train and vehicle loads under the test bridge as vibration sources. Through a laboratory and a filed test, the effectiveness and the applicability of the proposed methods were verified.

본 연구에서는 교통하중에 의한 상시진동 데이터를 이용하여 신경망기법기반 교량의 손상추정을 수행하였다. 상시진동시험에 의한 손상추정의 과정은 일반적으로 교량 개통초기 손상전 측정기록에 의한 기저 유한요소모델의 개선, 공용하중하의 상시진동 데이터의 획득, 모드계수의 추정, 손상 위치 및 정도의 추정으로 이루어진다. 본 연구에서는 기저 유한요소모델에 모델링 오차가 존재한다는 가정하에, 모델링오차에 민감하지 않은 모드계수를 신경망의 입력으로 사용하여 해석모드와 실험모드의 불일치에 의한 손상추정 오차를 줄이고자 하였다. 실내실험에서는 통행 차량하중을 모사하기 위하여 모형차량을 이용하여 주행실험을 수행하였다. 한남대교에 대한 현장실험에서는 인접교량을 통행하는 차량하중과 대상교량 하부를 통과하는 열차 및 차량하중을 진동원으로 이용하여 상시진동시험을 수행하였다. 실내실험과 현장실험을 통하여 본 연구의 효율성과 적용성을 검증하였다.

Keywords

References

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