Smart Structures and Systems

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Condition assessment of stay cables through enhanced time series classification using a deep learning approach

  • Zhang, Zhiming (School for Engineering of Matter, Transport and Energy, Arizona State University) ;
  • Yan, Jin (Palo Alto Research Center) ;
  • Li, Liangding (Department of Computer Science, University of Central Florida) ;
  • Pan, Hong (Department of Civil and Environmental Engineering, North Dakota State University) ;
  • Dong, Chuanzhi (Department of Civil, Environmental, and Construction Engineering, University of Central Florida)
  • Received : 2021.04.14
  • Accepted : 2021.07.22
  • Published : 2022.01.25
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Abstract

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

Keywords

Acknowledgement

Our team was awarded the 1st prize in the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020) for the work presented in this paper. The authors appreciate the essential support from the organizing committee of IPC-SHM 2020 during this competition. More information about this competition can be found in Bao et al. (2021), IPC. Additionally, the authors would like to acknowledge the assistance from Mr. Nan Xu, a graduate research assistant at Arizona State University, in visualizing the time series data using the manifold learning method.

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