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On Damage Identification in Civil Structures Using Tensor Analysis

  • Nguyen Lu Dang KhoaEmail author
  • Bang Zhang
  • Yang Wang
  • Wei Liu
  • Fang Chen
  • Samir Mustapha
  • Peter Runcie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9077)

Abstract

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.

Keywords

Tensor analysis Structural health monitoring Damage identification Unsupervised learning 

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References

  1. 1.
    Acar, E., Yener, B.: Unsupervised multiway data analysis: A literature survey. IEEE Transactions on Knowledge and Data Engineering 21(1), 6–20 (2009)CrossRefGoogle Scholar
  2. 2.
    Acar, E., Aykut-Bingol, C., Bingol, H., Bro, R., Yener, B.: Multiway analysis of epilepsy tensors. Bioinformatics 23(13), i10–i18 (2007)CrossRefGoogle Scholar
  3. 3.
    Andersson, C.A., Bro, R.: The n-way toolbox for MATLAB. Chemometrics and Intelligent Laboratory Systems 52(1), 1–4 (2000)CrossRefGoogle Scholar
  4. 4.
    Bader, B.W., Kolda, T.G., et al.: Matlab tensor toolbox version 2.5 (January 2012). http://www.sandia.gov/tgkolda/TensorToolbox/
  5. 5.
    Bro, R., Kiers, H.A.L.: A new efficient method for determining the number of components in parafac models. Journal of Chemometrics 17(5), 274–286 (2003)CrossRefGoogle Scholar
  6. 6.
    Chan, T.H., Ni, Y.Q., Ko, J.M.: Neural network novelty filtering for anomaly detection. In: Cheng, F. (ed.) 2nd International Workshop on Structural Health Monitoring, pp. 133–137. Technomic Pub. Co., Standford (1999)Google Scholar
  7. 7.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  8. 8.
    Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365(1851), 303–315 (2007)CrossRefGoogle Scholar
  9. 9.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Kolda, T.G., Sun, J.: Scalable tensor decompositions for multi-aspect data mining. In: ICDM 2008: Proceedings of the 8th IEEE International Conference on Data Mining, pp. 363–372 (December 2008)Google Scholar
  11. 11.
    LANL: Los alamos national laboratory website (2013). http://institute.lanl.gov/ei/software-and-data/ (last visited January 6, 2013)
  12. 12.
    Liu, W., Chan, J., Bailey, J., Leckie, C., Kotagiri, R.: Utilizing common substructures to speedup tensor factorization for mining dynamic graphs. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, pp. 435–444. ACM, New York (2012)Google Scholar
  13. 13.
    Prada, M.A., Toivola, J., Kullaa, J., Hollmn, J.: Three-way analysis of structural health monitoring data. Neurocomputing 80, 119–128 (2012). Special Issue on Machine Learning for Signal Processing 2010CrossRefGoogle Scholar
  14. 14.
    Rytter, A.: Vibration-based inspection of civil engineering structures. Ph.D. thesis, University of Aalborg, Denmark (1993)Google Scholar
  15. 15.
    Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: NIPS, pp. 582–588 (1999)Google Scholar
  16. 16.
    Sun, J., Tao, D., Papadimitriou, S., Yu, P.S., Faloutsos, C.: Incremental tensor analysis: Theory and applications. ACM Trans. Knowl. Discov. Data 2(3), 11:1–11:37 (2008)CrossRefGoogle Scholar
  17. 17.
    Worden, K., Manson, G., Fieller, N.: Damage detection using outlier analysis. Journal of Sound and Vibration 229(3), 647–667 (2000)CrossRefGoogle Scholar
  18. 18.
    Worden, K., Manson, G.: The application of machine learning to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365(1851), 515–537 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nguyen Lu Dang Khoa
    • 1
    Email author
  • Bang Zhang
    • 1
  • Yang Wang
    • 1
  • Wei Liu
    • 2
  • Fang Chen
    • 1
  • Samir Mustapha
    • 3
  • Peter Runcie
    • 1
  1. 1.National ICT AustraliaEveleighAustralia
  2. 2.Advanced Analytics InstituteUniversity of TechnologySydneyAustralia
  3. 3.Department of Mechanical EngineeringAmerican University of BeirutBeirutLebanon

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