Smart Structures and Systems

테크노프레스 (Techno-Press)
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Multi-stage structural damage diagnosis method based on "energy-damage" theory

  • Yi, Ting-Hua (School of Cvil Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Li, Hong-Nan (School of Cvil Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Sun, Hong-Min (School of Civil Engineering, Shenyang Jianzhu University)
  • 투고 : 2012.07.12
  • 심사 : 2012.11.07
  • 발행 : 2013.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

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참고문헌

  1. Basheer, I.A. and Hajmeer, M. (2000), "Artificial neural networks: Fundamentals, computing, design, and application", J. Microbiol. Meth., 43(1), 3-31. https://doi.org/10.1016/S0167-7012(00)00201-3
  2. Boller, C., Chang, F.K. and Fujino, Y. (2009), Encyclopedia of structural health monitoring, John Wiley & Sons Ltd.
  3. Carden, E.P. and Brownjohn, J.M.W. (2008), "ARMA modelled time-series classification for structural health monitoring of civil infrastructure", Mech. Syst. Signal Pr., 22(2), 295-314. https://doi.org/10.1016/j.ymssp.2007.07.003
  4. Chen, B. and Zang, C.Z. (2009), "Artificial immune pattern recognition for structure damage classification", Comput. Struct., 87(21-22), 1394-1407. https://doi.org/10.1016/j.compstruc.2009.08.012
  5. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz, D.W. (1996), Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review, Los Alamos National Laboratory Report, LA-13070-MS.
  6. Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2004), "Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data", J. Eng. Mech. - ASCE, 130(1), 3-15. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(3)
  7. Lam, H.F. and Ng, C.T. (2008), "The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm", Eng. Struct., 30(10), 2762-2770. https://doi.org/10.1016/j.engstruct.2008.03.012
  8. Lam, H.F., Yuen, K.V. and Beck, J.L. (2006), "Structural health monitoring via measured Ritz vectors utilizing artificial neural networks", Comput. Aided Civ. Inf., 21(4), 232-241. https://doi.org/10.1111/j.1467-8667.2006.00431.x
  9. Lautour, O.R. and Omenzetter, P. (2010), "Damage classification and estimation in experimental structures using time series analysis and pattern recognition", Mech. Syst. Signal Pr., 24(5), 1556-1569. https://doi.org/10.1016/j.ymssp.2009.12.008
  10. Lei, Y., Jiang, Y.Q. and Xu, Z.Q. (2012), "Structural damage detection with limited input and output measurement signals", Mech. Syst. Signal Pr., 28(0), 229-243. https://doi.org/10.1016/j.ymssp.2011.07.026
  11. Li, H.N. and Sun, H.M. (2003), "Damage diagnosis of framework structure based on wavelet packet analysis and neural network", Earthq. Eng. Eng. Vib., 23(5), 141-148. https://doi.org/10.3969/j.issn.1000-1301.2003.05.023
  12. Li, H.N. and Yang H. (2007), "System identification of dynamic structure by the multi-branch BPNN", Neurocomputing, 70(4-6), 835-841. https://doi.org/10.1016/j.neucom.2006.10.015
  13. Misiti, M., Misiti, Y., Oppenheim, G. and Poggi, J.M. (2004), Wavelet toolbox for use with Matlab, User's Guide, Ver. 3.
  14. MATLAB, The MathWorks, Inc. Natwick, MA (USA), http://www.mathworks.com.
  15. Nair, K.K., Kiremidjian, A.S. and Law, K.H. (2006), "Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure", J. Sound Vib., 291(1-2), 349-368. https://doi.org/10.1016/j.jsv.2005.06.016
  16. Sohn, H., Farrar, C.R., Hunter, N.F. and Worden, K. (2001), "Structural health monitoring using statistical pattern recognition techniques", J. Dyn. Syst. Meas. Control T.- ASME, 23(4), 706-711.
  17. Song, Y.B. (2000), "Quick training method for multi-layer bp neural network and its application", Contr. Dec., 15(1), 125-127. https://doi.org/10.3321/j.issn:1001-0920.2000.01.035
  18. Sun, Z. and Chang, C.C. (2002), "Structural damage assessment based on wavelet packet transform", J. Struct. Eng. - ASCE, 128(10), 1354-1361. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:10(1354)
  19. Wenzel, H. (2009), Health monitoring of bridges, USA, John Wiley and Sons Ltd.
  20. Wickerhauser M.V. (1994), Adapted wavelet analysis-from theory to software, (Ed. A.K. Peters), Welleslay, MA, USA.
  21. Yang, L.N., Peng, L., Zhang, L.M., Zhang, L.L. and Yang S.S. (2009), "A prediction model for population occurrence of paddy stem borer based on back propagation artificial neural network and principal components analysis", Comput. Electron. Agr., 68(2), 200-206. https://doi.org/10.1016/j.compag.2009.06.003
  22. Yen, G.G. and Lin, K.C. (2000), "Wavelet packet feature extraction for vibration monitoring", IEEE T. Ind. Electron., 47(3), 650-667. https://doi.org/10.1109/41.847906
  23. Yi, T.H., Li, H.N. and Gu M. (2012), "Recent research and applications of GPS-based monitoring technology for high-rise structures", Struct. Health Monit., 20(5), 649-670.
  24. Zhang, H., Schulz, M.J., Ferguson, F. and Pai, P.F. (1999), "Structural health monitoring using transmittance functions", Mech. Syst. Signal Pr., 13(5), 765-787. https://doi.org/10.1006/mssp.1999.1228
  25. Zhang, L., Luo, J.H. and Yang S.Y. (2009), "Forecasting box office revenue of movies with BP neural network", Expert Syst. Appl., 36(3), 6580-6587. https://doi.org/10.1016/j.eswa.2008.07.064
  26. Zheng, H. and Mita, A. (2009), "Localized damage detection of structures subject to multiple ambient excitations using two distance measures for autoregressive models", Struct. Health Monit., 8(3), 207-222. https://doi.org/10.1177/1475921708102145

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