On Damage Identification in Civil Structures Using Tensor Analysis

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• 作者：Nguyen Lu Dang Khoa (10)
  Bang Zhang (10)
  Yang Wang (10)
  Wei Liu (11)
  Fang Chen (10)
  Samir Mustapha (12)
  Peter Runcie (10)
  
  10. National ICT Australia ; Eveleigh ; NSW ; 2015 ; Australia
  11. Advanced Analytics Institute ; University of Technology ; Sydney ; Australia
  12. Department of Mechanical Engineering ; American University of Beirut ; Beirut ; Lebanon
  
• 关键词：Tensor analysis ; Structural health monitoring ; Damage identification ; Unsupervised learning
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9077
• 期：1
• 页码：459-471
• 全文大小：345 KB
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• 作者单位：Advances in Knowledge Discovery and Data Mining
• 丛书名：978-3-319-18037-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. In structural health monitoring, the data are usually highly redundant and correlated. The measured variables are not only correlated with each other at a certain time but also are autocorrelated themselves over time. Matrix-based two-way analysis, which is usually used in structural health monitoring, can not capture all these relationships and correlations together. Tensor analysis allows us to analyse the vibration data in temporal, spatial and feature modes at the same time. In our approach, we use tensor analysis and one-class support vector machine for damage detection, localization and estimation in an unsupervised manner. The method shows promising results using data from lab-based structures and also data collected from the Sydney Harbour Bridge, one of iconic structures in Australia. We can obtain a damage detection accuracy of 0.98 and higher for all the data. Locations of damage were captured correctly and different levels of damage severity were well estimated.