Smart Structures and Systems

  • 제29권1호
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  • Pages.17-28
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Damaged cable detection with statistical analysis, clustering, and deep learning models

  • Son, Hyesook (Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University) ;
  • Yoon, Chanyoung (Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University) ;
  • Kim, Yejin (Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University) ;
  • Jang, Yun (Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University) ;
  • Tran, Linh Viet (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Seung-Eock (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Dong Joo (Department of Civil and Environmental Engineering, Sejong University) ;
  • Park, Jongwoong (School of Civil and Environmental Engineering, Urban Design and Studies, Chung-Ang University)
  • 투고 : 2021.03.21
  • 심사 : 2021.10.04
  • 발행 : 2022.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

This work was supported in part by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT under Grant 2019R1A4A1021702, and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2021-0-02076, Developing Reasoning AI Engine in Complex Systems (REX) and its Applications).

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