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논문 기본 정보

자료유형
학술저널
저자정보
Chunfeng Wan (Southeast University) Songtao Xue (Tongji University) Huachen Jiang (Shanghai Key Laboratory of Engineering Structure Safety) Liyu Xie (Tongji University) Da Fang (Southeast University) Shuai Gao (Southeast University) Kang Yang (Shanghai Institute of Technology) YouLiang Ding (Southeast University)
저널정보
국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal 제33권 제3호
발행연도
2024.3
수록면
189 - 199 (11page)

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To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicleinduced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

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