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RISS 인기검색어
- 검색키워드
- 저자 : Youliang Fang (검색결과 2 건)
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Damage detection of bridge structures under moving loads based on CEEMD and PSD sensitivity analysis
Youliang Fang,Jie Xing,Xueting Liu,Danyang Liu,Ying Zhang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.7
This study proposes a new damage identification method based on a combination of complete ensemble empirical mode decomposition (CEEMD) and power spectrum density (PSD) sensitivity analysis to analyze the acceleration signals of bridge structures under moving loads and achieve damage detection of bridge structures. This paper has achieved the ability to accurately identify the location of cracks and the extent of the damage along a girder with only one acceleration sensor arrangement. The measured data is processed by the CEEMD method. The damage location is revealed by directly examining the first-order intrinsic mode function corresponding to the highest-order pseudo-frequency component, which presents an abrupt change at the damage location. Secondly, after determining the damage location of the bridge, only the power spectrum sensitivity analysis of the crack parameters at the damage location is required to obtain the damage level, avoiding the need to blindly solve the power spectrum for all elements. Finally, the identification method is validated by considering environmental noise, damage locations, and crack depths. The numerical simulation results and experiments for various working conditions show that the method adopted in this paper has good identification capability in identifying cracks in bridge structures.
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Operational performance evaluation of bridges using autoencoder neural network and clustering
Chunfeng Wan,Songtao Xue,Huachen Jiang,Liyu Xie,Da Fang,Shuai Gao,Kang Yang,YouLiang Ding 국제구조공학회 2024 Smart Structures and Systems, An International Jou Vol.33 No.3
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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