Browse > Article
http://dx.doi.org/10.12989/sss.2013.12.3_4.345

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)
Publication Information
Smart Structures and Systems / v.12, no.3_4, 2013 , pp. 345-361 More about this Journal
Abstract
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.
Keywords
damage diagnosis; energy-damage theory; wavelet packet analysis; BP neural network; benchmark structure;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.   DOI   ScienceOn
2 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.   DOI
3 Misiti, M., Misiti, Y., Oppenheim, G. and Poggi, J.M. (2004), Wavelet toolbox for use with Matlab, User's Guide, Ver. 3.
4 MATLAB, The MathWorks, Inc. Natwick, MA (USA), http://www.mathworks.com.
5 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.   DOI   ScienceOn
6 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.
7 Song, Y.B. (2000), "Quick training method for multi-layer bp neural network and its application", Contr. Dec., 15(1), 125-127.   DOI
8 Sun, Z. and Chang, C.C. (2002), "Structural damage assessment based on wavelet packet transform", J. Struct. Eng. - ASCE, 128(10), 1354-1361.   DOI   ScienceOn
9 Wenzel, H. (2009), Health monitoring of bridges, USA, John Wiley and Sons Ltd.
10 Wickerhauser M.V. (1994), Adapted wavelet analysis-from theory to software, (Ed. A.K. Peters), Welleslay, MA, USA.
11 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.   DOI   ScienceOn
12 Yen, G.G. and Lin, K.C. (2000), "Wavelet packet feature extraction for vibration monitoring", IEEE T. Ind. Electron., 47(3), 650-667.   DOI   ScienceOn
13 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.
14 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.
15 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.   DOI   ScienceOn
16 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.   DOI   ScienceOn
17 Chen, B. and Zang, C.Z. (2009), "Artificial immune pattern recognition for structure damage classification", Comput. Struct., 87(21-22), 1394-1407.   DOI   ScienceOn
18 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.   DOI
19 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.   DOI   ScienceOn
20 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.   DOI   ScienceOn
21 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.   DOI   ScienceOn
22 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.   DOI   ScienceOn
23 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.   DOI
24 Li, H.N. and Yang H. (2007), "System identification of dynamic structure by the multi-branch BPNN", Neurocomputing, 70(4-6), 835-841.   DOI   ScienceOn
25 Basheer, I.A. and Hajmeer, M. (2000), "Artificial neural networks: Fundamentals, computing, design, and application", J. Microbiol. Meth., 43(1), 3-31.   DOI   ScienceOn
26 Boller, C., Chang, F.K. and Fujino, Y. (2009), Encyclopedia of structural health monitoring, John Wiley & Sons Ltd.