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CLC number: TU470

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Received: 2005-02-20

Revision Accepted: 2005-03-31

Crosschecked: 0000-00-00

Cited: 2

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.7 P.663~669

10.1631/jzus.2005.A0663


Experimental study of structural damage identification based on WPT and coupling NN


Author(s):  GUO Jian, CHEN Yong, SUN Bing-nan

Affiliation(s):  Department of Civil Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   jianguoch@sohu.com

Key Words:  Damage identification, Experimental study, Wavelet packet transform (WPT), Coupling neural network (NN)


GUO Jian, CHEN Yong, SUN Bing-nan. Experimental study of structural damage identification based on WPT and coupling NN[J]. Journal of Zhejiang University Science A, 2005, 6(7): 663~669.

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author="GUO Jian, CHEN Yong, SUN Bing-nan",
journal="Journal of Zhejiang University Science A",
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year="2005",
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doi="10.1631/jzus.2005.A0663"
}

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%A GUO Jian
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%A SUN Bing-nan
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%V 6
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%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0663

TY - JOUR
T1 - Experimental study of structural damage identification based on WPT and coupling NN
A1 - GUO Jian
A1 - CHEN Yong
A1 - SUN Bing-nan
J0 - Journal of Zhejiang University Science A
VL - 6
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SP - 663
EP - 669
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0663


Abstract: 
Too many sensors and data information in structural health monitoring system raise the problem of how to realize multi-sensor information fusion. An experiment on a three-story frame structure was conducted to obtain vibration test data in 36 damage cases. A coupling neural network (NN) based on multi-sensor information fusion is proposed to achieve identification of damage occurrence, damage localization and damage quantification, respectively. First, wavelet packet transform (WPT) is used to extract features of vibration test data from structure with different damage extent. Then, data fusion is conducted by assembling feature vectors of different type sensors. Finally, three sets of coupling NN are constructed to implement decision fusion and damage identification. The results of experimental study proved the validity and feasibility of the proposed methodology.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[3] Guo, J., 2004. Study of Structural Damage Identification Based on Wavelet Analysis. Ph.D Thesis, Zhejiang University, Hangzhou, China (in Chinese).

[4] Guo, J., Sun, B.N., 2005. Multi-scale analysis based on wavelet transform in bridge health monitoring. Journal of Zhejiang University (Engineering Science), 39(1):114-119 (in Chinese).

[5] Ni, Y.Q., Wang, B.S., Ko, J.M., 2002. Constructing input vectors to neural networks for structural damage identification. Smart Materials and Structures, 11(6):825-833.

[6] Qu, W.L., Chen, W., Li, Q.S., 2003. Two-step approach for joints damage diagnosis of framed structures by artificial neural networks. China Civil Engineering Journal, 36(50):37-45 (in Chinese).

[7] Sun, Z., Chang, C.C., 2002. Structural damage assessment based on wavelet packet transform. Journal of Structural Engineering, 128(10):1354-1361.

[8] Teng, J., Liu, H.J., Qu, W.L., 2004. Error Compensation Technique for the Structural Health Monitoring Sensor Systems. Proceedings of the Eighth International Symposium on Structural Engineering for Young Exports. Science Press, Xi’an, China, p.345-365.

[9] Yen, G.G., Lin, K.C., 2000. Wavelet packet feature extraction for vibration monitoring. IEEE Transaction on Industrial Electronics, 47(3):650-667.

[10] Wang, M.L., Heo, G., Satpathi, D., 1998. A health monitoring system for large structural systems. Smart Materials & Structures, 7(5):606-616.

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