A clustering approach for structural health monitoring on bridges

  • Alberto Diez
  • Nguyen Lu Dang Khoa
  • Mehrisadat Makki Alamdari
  • Yang Wang
  • Fang Chen
  • Peter Runcie
Original Paper
First Online:
Received:
Revised:
Accepted:

Abstract

Structural health monitoring is a process for identifying damage in civil infrastructures using sensing system. It has been increasingly employed due to advances in sensing technologies and data analytic using machine learning. A common problem within this scenario is that limited data of real structural faults are available. Therefore, unsupervised and novelty detection machine learning methods must be employed. This work presents a clustering based approach to group substructures or joints with similar behaviour on bridge and then detect abnormal or damaged ones, as part of efforts in applying structural health monitoring to the Sydney Harbour Bridge, one of iconic structures in Australia. The approach is a combination of feature extraction, a nearest neighbor based outlier removal, followed by a clustering approach over both vibration events and joints representatives. Vibration signals caused by passing vehicles from different joints are then classified and damaged joints can be detected and located. The validity of the approach was demonstrated using real data collected from the Sydney Harbour Bridge. The clustering results showed correlations among similarly located joints in different bridge zones. Moreover, it also helped to detect a damaged joint and a joint with a faulty instrumented sensor, and thus demonstrated the feasibility of the proposed clustering based approach to complement existing damage detection strategies.

Keywords

Structural health monitoring Damage detection Novelty detection Unsupervised learning K-means clustering 
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Notes

Acknowledgments

The main author would like to thank National ICT Australia for the great support to this work and during his internship, as part of the work on his diploma thesis. The authors also wish to thank the Road and Maritime Services (RMS) in New South Wales for provision of the support and testing facilities for this research work.

References

  1. 1.
    Aggarwal CC, Yu PS (2013) Proximity-based outlier detection. In: Outlier analysis. Springer, New York, pp 101–133CrossRefGoogle Scholar
  2. 2.
    Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185MathSciNetGoogle Scholar
  3. 3.
    Brincker R, Zhang L, Andersen P (2000) Modal identification from ambient responses using frequency domain decomposition. In: Proceedings of 18th international modal analysis conference—IMAC, pp 625–630Google Scholar
  4. 4.
    Cao G, Bouman C (2009) Covariance estimation for high dimensional data vectors using the sparse matrix transform. In: Advances in neural information processing systems, vol 21, pp 225–232Google Scholar
  5. 5.
    Carden EP, Fanning P (2004) Vibration based condition monitoring: a review. Struct Health Monit 3(4):355–377CrossRefGoogle Scholar
  6. 6.
    Chang PC, Flatau A, Liu S (2003) Review paper: health monitoring of civil infrastructure. Struct Health Monit 2(3):257–267CrossRefGoogle Scholar
  7. 7.
    Cho S-J (2011) Structural health monitoring of cable-stayed bridge using wireless smart sensors. Ph. D. Dissertation, KAIST, Daejeon, KoreaGoogle Scholar
  8. 8.
    Collings D (2008) Lessons from historical bridge failures. Proc Inst Civ Eng-Civ Eng 161(6):20–27Google Scholar
  9. 9.
    Cooley JW, Tukey JW (1965) An algorithm for the machine calculation of complex fourier series. Math Comput 19(90):297–301MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27CrossRefMATHGoogle Scholar
  11. 11.
    Doebling S, Farrar C, Prime M, Shevitz D (1996) Damage identification in structures and mechanical systems based on changes in their vibration characteristics: a detailed literature survey. Los Alamos National Laboratory, Rep No LA-13070-MSGoogle Scholar
  12. 12.
    Farrar CR, Duffey TA, Doebling SW, Nix DA (1999) A statistical pattern recognition paradigm for vibration-based structural health monitoring. Struct Health Monit 2000:764–773Google Scholar
  13. 13.
    Farrar CR, Worden K (2007) An introduction to structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):303–315CrossRefGoogle Scholar
  14. 14.
    Fourier J (1820) Méemoire sur le refroidissement séeculaire du globe terrestre. Ann Chim Phys (2) 13:418–437Google Scholar
  15. 15.
    Frangopol DM, Liu M (2007) Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost. Struct Infrastruct Eng 3(1):29–41CrossRefGoogle Scholar
  16. 16.
    Fugate ML, Sohn H, Farrar CR (2000) Unsupervised learning methods for vibration-based damage detection. In: Proceedings of 18th international modal analysis conference—IMAC, p 18Google Scholar
  17. 17.
    Gul M, Necati Catbas F (2009) Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications. Mech Syst Signal Process 23(7):2192–2204CrossRefGoogle Scholar
  18. 18.
    Irwin PA, Stoyanoff S, Xie J, Hunter M (2005) Tacoma narrows 50 years laterwind engineering investigations for parallel bridges. Bridg Struct 1(1):3–17CrossRefGoogle Scholar
  19. 19.
    Jafarkhani R, Masri SF (2011) Finite element model updating using evolutionary strategy for damage detection. Comput Aided Civ Infrastruct Eng 26(3):207–224CrossRefGoogle Scholar
  20. 20.
    Jones E, Oliphant T, Peterson P et al (2001) SciPy: open source scientific tools for Python. Retrieved from http://www.scipy.org/
  21. 21.
    Lederman G, Wang Z, Bielak J, Noh H, Garrett J, Chen S et al (2014) Damage quantification and localization algorithms for indirect SHM of bridges. In: Proceedings of the international conference on bridge maintenance, safety management, Shanghai, ChinaGoogle Scholar
  22. 22.
    Lichtenstein AG (1993) The silver bridge collapse recounted. J Perform Constr Facil 7(4):249–261CrossRefGoogle Scholar
  23. 23.
    Maeck J, Peeters B, De Roeck G (2001) Damage identification on the z24 bridge using vibration monitoring. Smart Mater Struct 10(3):512CrossRefGoogle Scholar
  24. 24.
    Moore AW (1991) An introductory tutorial on kd-trees. Technical Report No. 209, Computer Laboratory, University of CambridgeGoogle Scholar
  25. 25.
    Newland DE (2012) An introduction to random vibrations, spectral & wavelet analysis, 3rd edn. Dover Publications Inc, New YorkGoogle Scholar
  26. 26.
    Nie P, Li B (2011) A cluster-based data aggregation architecture in WSN for structural health monitoring. In: 7th International wireless communications and mobile computing conference, 2011. IWCMC 2011, pp 546–552Google Scholar
  27. 27.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830MathSciNetMATHGoogle Scholar
  28. 28.
    Santos A, Figueiredo E, Costa J (2015) Clustering studies for damage detection in bridges: a comparison study. Struct Health Monit 2015Google Scholar
  29. 29.
    Sibly P, Walker A, Stephenson R, Moisseiff L et al (1977) Structural accidents and their causes, l. Proc Inst Civ Eng 62:191–208Google Scholar
  30. 30.
    Sohn H, Farrar CR, Hemez FM, Shunk DD, Stinemates DW, Nadler BR et al (2004) A review of structural health monitoring literature: 1996–2001. Los Alamos National Laboratory, Los AlamosGoogle Scholar
  31. 31.
    Toivola J, Prada MA, Hollméen J (2010) Novelty detection in projected spaces for structural health monitoring. In: Advances in intelligent data analysis ix. Springer, Berlin, pp 208–219Google Scholar
  32. 32.
    Tsai D-M, Lin C-T, Chen J-F (2003) The evaluation of normalized cross correlations for defect detection. Pattern Recognit Lett 24(15):2525–2535CrossRefMATHGoogle Scholar
  33. 33.
    Wei WW-S (1994) Time series analysis. Addison-Wesley, ReadingGoogle Scholar
  34. 34.
    Wenzel H (2008) Health monitoring of bridges. Wiley, New YorkGoogle Scholar
  35. 35.
    Worden K, Manson G (2007) The application of machine learning to structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):515–537CrossRefGoogle Scholar
  36. 36.
    Wu W, Xiong H, Shekhar S (2004) Clustering and information retrieval, vol 11. Springer, BrelinCrossRefGoogle Scholar
  37. 37.
    Yeung W, Smith J (2005) Damage detection in bridges using neural networks for pattern recognition of vibration signatures. Eng Struct 27(5):685–698CrossRefGoogle Scholar
  38. 38.
    Yin A, Wang B, Hu X, Dai Z (2009) MHop-CL: a clustering protocol for bridge structure health monitoring system. In: International symposium on computer network and multimedia technology, 2009. CNMT 2009, pp 1–4Google Scholar
  39. 39.
    Yu L, Zhu J-H, Yu L-L (2013) Structural damage detection in a truss bridge model using fuzzy clustering and measured FRF data reduced by principal component projection. Adv Struct Eng 16(1):207–218CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Alberto Diez
    • 1
  • Nguyen Lu Dang Khoa
    • 2
  • Mehrisadat Makki Alamdari
    • 2
  • Yang Wang
    • 2
  • Fang Chen
    • 2
  • Peter Runcie
    • 2
  1. 1.Tecnalia Research & InnovationDonostia-San SebastiánSpain
  2. 2.National ICT Australia (NICTA)SydneyAustralia

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