The Pattern Recognition of Structural Damage Based on Wavelet Kernel Function SVM

Abstract:

This paper presents the pattern recognition method of structural damage based on least square wavelet kernel function-SVM, which uses limited data about change rate of natural frequency. The method remedies the shortages of traditional methods, which are insensitive to the natural frequency. The result of numerical simulation indicates that the method has favorable recognition precision, better anti-noise capability, and well robustness.

• Periodical: Advanced Materials Research (Volumes 446-449)
• Pages: 3523-3528
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.446-449.3523
• Online since: January 2012
• Authors: Mao Yu Zhang, Bo Li, Yan Ru Wang, Jian Fang Zhang
• Keywords: Pattern Recognition, Structural Damage, Support Vector Machine (SVM), Wavelet Kernel Function

References

[1] Narkis, Y. Identification of crack location in vibrating simply supported beams [J]. Journal of Sound and Vibration，1994，172(4): 549-558.

DOI: 10.1006/jsvi.1994.1195

[2] LI Hongnan, LI Dongsheng. Safety assessment, health monitoring and damage diagnosis for structures in civil engineering [J]. Earthquake Engineering and Engineering Vibration, 2002，22（3）：82-90.(in Chinese)

[3] He J, Ewins D J. Analytical stiffness matrix correction using measured vibration modes [J]. Modal Analysis: The International Journal of Analytical and Experimental Modal Analysis, 1986, 1(3): 9-14.

[4] Park, Y S, Park H S, Lee S S. Weighted-error-matrix application to detect stiffness damage-characteristic measurement [J]. Modal Analysis: The International Journal of Analytical and Experimental Modal Analysis, 1988, 3(3): 101-107.

[5] Hoshiya M, Maruyama O. Identification of a restoring force model by EK-WGI procedure [J]. Structures and stochastic methods，1992，112-122.

DOI: 10.1016/b978-0-444-98955-0.50013-6

[6] Ghanem R, Shinozuka M. Structural system identification I: theory [J]. Journal of Engineering Mechanics, 1995, 121 (2): 255-264.

DOI: 10.1061/(asce)0733-9399(1995)121:2(255)

[7] Shinozuka M, Ghanem R. Structural system identification II: experimental verification [J]. Journal of Engineering Mechanics, 1995, 121 (2): 265-273.

DOI: 10.1061/(asce)0733-9399(1995)121:2(265)

[8] Vapnik V. Statistical Learning Theory [M]. NewYork: John Wiley &Sons, 1998.

[9] Burges C J C. A tutorial on support vector machines for pattern recognition [J]. Data Mining and Knowledge Discovery，1998，2(2):121-167.

[10] TANG Hesheng, XUE Songtao, CHEN Rong, JIN Kan. Sequential LS-SVM for structural systems identification [J]. Journal of Vibration Engineering, 2006，19（3）：382-386.(in Chinese)

[11] Fletcher R. Practical Methods of Optimization [M]. John Wiley and Sons: Chichester and New York, 1987.

[12] NELLO Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods [M]. Beijing: Publishing House of Electronics Industry, 2004. (in Chinese)

DOI: 10.1017/cbo9780511801389

[13] CUI Wanzhao, ZHU Changchun, BAO Wenxing, LIU Junhua. Least Squares Wavelet Support Vector Machines and Its Application to Nonlinear System Identification [J]. Journal of Xi'an Jiaotong University, 2004，38(6): 562-565. (in Chinese)

[14] ZHANG Maoyu. The Application of Support Vector Machine Method to Structural Damage Identification [D]. Shanghai: Tongji University, 2007. (in Chinese)