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Smart Structures and Systems
  Volume 30, Number 6, December 2022 , pages 661-671
DOI: https://doi.org/10.12989/sss.2022.30.6.661
 


Cable anomaly detection driven by spatiotemporal correlation dissimilarity measurements of bridge grouped cable forces
Dong-Hui Yang, Hai-Lun Gu, Ting-Hua Yi and Zhan-Jun Wu

 
Abstract
    Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.
 
Key Words
    anomaly location; condition assessment; grouped cable forces; novelty detection; spatiotemporal correlation
 
Address
(1) Dong-Hui Yang:
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
(2) Dong-Hui Yang, Hai-Lun Gu, Ting-Hua Yi:
School of Civil Engineering, Dalian University of Technology, Dalian 116023, China;
(3) Zhan-Jun Wu:
State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, China.
 

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