Smart Structures and Systems

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Unsupervised one-class classification for condition assessment of bridge cables using Bayesian factor analysis

  • Wang, Xiaoyou (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Li, Lingfang (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Tian, Wei (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Du, Yao (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Hou, Rongrong (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Xia, Yong (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • Received : 2021.04.02
  • Accepted : 2021.07.28
  • Published : 2022.01.25
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Abstract

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The single-peaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

Keywords

Acknowledgement

The authors are grateful to the organisers of the 1st International Project Competition for SHM (IPC-SHM, 2020) for generously providing the excellent opportunities during the COVID-19 and invaluable data from the actual structures. Our gratitude goes to Professor Hui Li and Professor Billie F. Spencer Jr., Chairs of IPC-SHM, 2020. This research is also supported by the Key-Area Research and Development Program of Guangdong Province (Project No. 2019B111106001) and Research Grants Council of HKSAR-General Research Fund (Project No. 15201920).

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