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Smart Structures and Systems Volume 29, Number 1, January 2022 , pages 17-28 DOI: https://doi.org/10.12989/sss.2022.29.1.017 |
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Damaged cable detection with statistical analysis, clustering, and deep learning models |
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Hyesook Son, Chanyoung Yoon, Yejin Kim, Yun Jang, Linh Viet Tran, Seung-Eock Kim, Dong Joo Kim and Jongwoong Park
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| Abstract | ||
| The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge. | ||
| Key Words | ||
| anomaly detection; clustering; deep learning; LSTM; time series | ||
| Address | ||
| (1) Hyesook Son, Chanyoung Yoon, Yejin Kim, Yun Jang: Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University, 209 Neungdong-ro, Gwangjin-gu , Seoul, Republic of Korea; (2) Linh Viet Tran, Seung-Eock Kim, Dong Joo Kim: Department of Civil and Environmental Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul, Republic of Korea; (3) Jongwoong Park: School of Civil and Environmental Engineering, Urban Design and Studies, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea. | ||