Browse > Article
http://dx.doi.org/10.12989/sss.2013.12.3_4.327

Theoretical and experimental study on damage detection for beam string structure  

He, Haoxiang (Beijing Laboratory of Earthquake Engineering and Structural Retrofit, Beijing University of Technology)
Yan, Weiming (Beijing Laboratory of Earthquake Engineering and Structural Retrofit, Beijing University of Technology)
Zhang, Ailin (Beijing Laboratory of Earthquake Engineering and Structural Retrofit, Beijing University of Technology)
Publication Information
Smart Structures and Systems / v.12, no.3_4, 2013 , pp. 327-344 More about this Journal
Abstract
Beam string structure (BSS) is introduced as a new type of hybrid prestressed string structures. The composition and mechanics features of BSS are discussed. The main principles of wavelet packet transform (WPT), principal component analysis (PCA) and support vector machine (SVM) have been reviewed. WPT is applied to the structural response signals, and feature vectors are obtained by feature extraction and PCA. The feature vectors are used for training and classification as the inputs of the support vector machine. The method is used to a single one-way arched beam string structure for damage detection. The cable prestress loss and web members damage experiment for a beam string structure is carried through. Different prestressing forces are applied on the cable to simulate cable prestress loss, the prestressing forces are calculated by the frequencies which are solved by Fourier transform or wavelet transform under impulse excitation. Test results verify this method is accurate and convenient. The damage cases of web members on the beam are tested to validate the efficiency of the method presented in this study. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction method. The feature vectors are used for training and classification as the inputs of the support vector machine. The structural damage position and degree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.
Keywords
beam string structure; damage detection; wavelet packet decomposition; support vector machine; principle component analysis;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Wu, X., Gbaboussi, J. and Garrett, J.H. (1992), "Use of neural network in detection of structural damage", Comput. Struct., 42(2), 649-659.   DOI   ScienceOn
2 Xue, W.C. and Liu, S. (2009), "Design optimization and experimental study on beam string structures", J. Constr. Steel Res., 65(1), 70-80.   DOI   ScienceOn
3 Yao, G.C., Chang K.C. and Lee, G.C. (1992), "Damage diagnosis of steel frames using vibrational signature analysis", J. Eng. Mech. - ASCE, 118(9), 1949-1961.   DOI
4 Yao, R.G. and Pakzad, S.N. (2012), "Autoregressive statistical pattern recognition algorithms for damage detection in civil structures", Mech. Syst. Signal Pr., 31(3), 355-368.   DOI   ScienceOn
5 Yan, Y. J., Cheng L. and Yam, L.H. (2007), "Development in vibration based structural damage detection technique", Mech. Syst. Signal Pr., 21(5), 2198-2211.   DOI   ScienceOn
6 Yi, T.H., Li, H.N. and Gu, M. (2011), "Characterization and extraction of global positioning system multipath signals using improved particle filtering algorithm", Meas. Sci. Technol., 22, 1-11.
7 Yi, T.H., Li, H.N. and Zhao, X.Y. (2012), "Noise smoothing for structural vibration test signals using an improved wavelet thresholding technique", Sensors, 12(8), 11205-11220.   DOI   ScienceOn
8 Yi, T.H., Li, H.N. and Gu, M. (2013), "Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge", Measurement, 46(1), 420-432.   DOI   ScienceOn
9 Yuen, M.M.F. (1985), "A numerical study of the eigenparameters of a damaged cantilever", J. Sound Vib., 103(3), 301-310.   DOI   ScienceOn
10 Pandey, A.K. and Biswas, M. (1994), "Damage detection in structures using changes in flexibility", J. Sound Vib., 169 (1), 3-7.   DOI   ScienceOn
11 Peng, X.L., Hao H. and Li, Z.X. (2012), "Application of wavelet packet transform in subsea pipeline bedding condition assessment", Eng. Struct., 39(6), 50-65.   DOI   ScienceOn
12 Rizos, P.F. (1990), "Identification of crack location and magnitude in a cantilever beam from the vibration modes", J. Sound Vib., 138(3), 381-388.   DOI   ScienceOn
13 Saitoh, M. and Okada, A. (1999), "The role of string in hybrid string structure", Eng. Struct., 21(8), 756-769.   DOI   ScienceOn
14 Samanta, B., Al-Balushi, K.R and Al-Araimi, S.A. (2003), "Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection", Eng. Appl. Artif. Intell., 16(5), 657-665.   DOI   ScienceOn
15 Sampaio, R.P.C. (1999), "Damage detection using the frequency-response-function curvature method", J. Sound Vib., 226 (5), 1029-1042.   DOI   ScienceOn
16 Salawu, O.S. (1997), "Detection of structural damage through changes in frequency: a review", Eng. Struct., 19(9), 718-723.   DOI   ScienceOn
17 Sun, Z. and Chang, C.C. (2003). "Structural degradation monitoring using covariance-driven wavelet packet signature", Struct. Health Monit., 2(4), 309-325.   DOI
18 Wang, Q. and Deng, X.M. (1999), "Damage detection with spatial wavelets", Int. J. Solids Struct., 36(23), 3443-3468.   DOI   ScienceOn
19 Teboub, G.S.H. and Hajela, J.D. (1990), "Damage identification of a composite material beam based on neural networks", J. Sound Vib., 47(2), 608-617.
20 Vapnik, V. N. (1995), The nature of statistical learning theory, New York, Springer-Verlag.
21 Wu, M.E. (2008), "Analytical method for the lateral buckling of the struts in beam string structures", Eng. Struct., 30(9), 2301-2310.   DOI   ScienceOn
22 Amaravadi, V., Rao, V.S. and Mitchell, K. (2002), Structural integrity monitoring of composite patch repairs using wavelet analysis and neural network, NED for Health Monitoring and Diagnostics, San Diego, 4701-4717. CD Version.
23 Cristianini, N. and Shawe-Taylor, J. (2000), An introduction to support vector machines and other kernel-based learning methods, Cambridge, UK: Cambridge University Press.
24 Fang, X., Luo H. and Tang, J (2005), "Structural damage detection using neural network with learning rate improvement", Comput. Struct., 83(25-26), 2150-2161.   DOI   ScienceOn
25 Hera, A. and Hou, Z.K. (2004), "Application of wavelet approach for ASCE structural health monitoring benchmark studies", J. Eng. Mech. - ASCE, 130(1), 96-104.   DOI   ScienceOn
26 Li, H.N., Yi, T.H. and Gu, M. (2009b), "Evaluation of earthquake-induced structural damages by wavelet transform", Prog. Nat. Sci., 19(4), 461-470.   DOI   ScienceOn
27 Housner, G.W., Bergman L.A. and Caughey T.K., Chassiakos, A., Claus, R., Masri, S., Skelton, R., Soong, T., Spencer, B. and Yao, J. (1997), "Structural control: past, present, and future", J. Eng. Mech. - ASCE, 123(9), 897-971.   DOI
28 Joliffe, I.T. (1986), Principal component analysis, New York, Springer-Verlag.
29 Li, H.N., He, X.Y. and Yi, T.H. (2009a), "Multi-component seismic response analysis of offshore platform by wavelet energy principle", Coast. Eng., 56(8), 810-830.   DOI   ScienceOn
30 Moyo, P. and Brownjohn, J.M.W. (2002), "Detection of anomalous structural behavior using wavelet analysis", Mech. Syst. Signal Pr., 16(2-3), 429-445.   DOI   ScienceOn