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A clustering approach for structural health monitoring on bridges

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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.

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References

  1. Aggarwal CC, Yu PS (2013) Proximity-based outlier detection. In: Outlier analysis. Springer, New York, pp 101–133

    Chapter  Google Scholar 

  2. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185

    MathSciNet  Google Scholar 

  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 625–630

  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 225–232

  5. Carden EP, Fanning P (2004) Vibration based condition monitoring: a review. Struct Health Monit 3(4):355–377

    Article  Google Scholar 

  6. Chang PC, Flatau A, Liu S (2003) Review paper: health monitoring of civil infrastructure. Struct Health Monit 2(3):257–267

    Article  Google Scholar 

  7. Cho S-J (2011) Structural health monitoring of cable-stayed bridge using wireless smart sensors. Ph. D. Dissertation, KAIST, Daejeon, Korea

  8. Collings D (2008) Lessons from historical bridge failures. Proc Inst Civ Eng-Civ Eng 161(6):20–27

    Google Scholar 

  9. Cooley JW, Tukey JW (1965) An algorithm for the machine calculation of complex fourier series. Math Comput 19(90):297–301

    Article  MathSciNet  MATH  Google Scholar 

  10. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  MATH  Google Scholar 

  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-MS

  12. Farrar CR, Duffey TA, Doebling SW, Nix DA (1999) A statistical pattern recognition paradigm for vibration-based structural health monitoring. Struct Health Monit 2000:764–773

    Google Scholar 

  13. Farrar CR, Worden K (2007) An introduction to structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):303–315

    Article  Google Scholar 

  14. Fourier J (1820) Méemoire sur le refroidissement séeculaire du globe terrestre. Ann Chim Phys (2) 13:418–437

  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):29–41

    Article  Google Scholar 

  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 18

  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):2192–2204

    Article  Google Scholar 

  18. Irwin PA, Stoyanoff S, Xie J, Hunter M (2005) Tacoma narrows 50 years laterwind engineering investigations for parallel bridges. Bridg Struct 1(1):3–17

    Article  Google Scholar 

  19. Jafarkhani R, Masri SF (2011) Finite element model updating using evolutionary strategy for damage detection. Comput Aided Civ Infrastruct Eng 26(3):207–224

    Article  Google Scholar 

  20. Jones E, Oliphant T, Peterson P et al (2001) SciPy: open source scientific tools for Python. Retrieved from http://www.scipy.org/

  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, China

  22. Lichtenstein AG (1993) The silver bridge collapse recounted. J Perform Constr Facil 7(4):249–261

    Article  Google Scholar 

  23. Maeck J, Peeters B, De Roeck G (2001) Damage identification on the z24 bridge using vibration monitoring. Smart Mater Struct 10(3):512

    Article  Google Scholar 

  24. Moore AW (1991) An introductory tutorial on kd-trees. Technical Report No. 209, Computer Laboratory, University of Cambridge

  25. Newland DE (2012) An introduction to random vibrations, spectral & wavelet analysis, 3rd edn. Dover Publications Inc, New York

  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 546–552

  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 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  28. Santos A, Figueiredo E, Costa J (2015) Clustering studies for damage detection in bridges: a comparison study. Struct Health Monit 2015

  29. Sibly P, Walker A, Stephenson R, Moisseiff L et al (1977) Structural accidents and their causes, l. Proc Inst Civ Eng 62:191–208

    Google Scholar 

  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 Alamos

  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 208–219

  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):2525–2535

    Article  MATH  Google Scholar 

  33. Wei WW-S (1994) Time series analysis. Addison-Wesley, Reading

  34. Wenzel H (2008) Health monitoring of bridges. Wiley, New York

    Google Scholar 

  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):515–537

    Article  Google Scholar 

  36. Wu W, Xiong H, Shekhar S (2004) Clustering and information retrieval, vol 11. Springer, Brelin

    Book  Google Scholar 

  37. Yeung W, Smith J (2005) Damage detection in bridges using neural networks for pattern recognition of vibration signatures. Eng Struct 27(5):685–698

    Article  Google Scholar 

  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 1–4

  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):207–218

    Article  Google Scholar 

Download references

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.

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Authors and Affiliations

  1. Tecnalia Research & Innovation, Donostia-San Sebastián, Gipuzkoa, Spain

    Alberto Diez

  2. National ICT Australia (NICTA), Sydney, Australia

    Nguyen Lu Dang Khoa, Mehrisadat Makki Alamdari, Yang Wang, Fang Chen & Peter Runcie

Authors
  1. Alberto Diez
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  2. Nguyen Lu Dang Khoa
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  3. Mehrisadat Makki Alamdari
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  4. Yang Wang
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  5. Fang Chen
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  6. Peter Runcie
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Correspondence to Nguyen Lu Dang Khoa.

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Diez, A., Khoa, N.L.D., Makki Alamdari, M. et al. A clustering approach for structural health monitoring on bridges. J Civil Struct Health Monit 6, 429–445 (2016). https://doi.org/10.1007/s13349-016-0160-0

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  • DOI: https://doi.org/10.1007/s13349-016-0160-0

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