Abstract
Structural damage detection is very important for identifying and diagnosing the nature of the damage in an early stage so as to reduce catastrophic failures and prolong the service life of structures. In this paper, a novel approach is presented that integrates independent component analysis (ICA) and support vector machine (SVM). The procedure involves extracting independent components from measured sensor data through ICA and then using these signals as input data for a SVM classifier. The experiment presented employs the benchmark data from the University of British Columbia to examine the effectiveness of the method. Results showed that the accuracy of damage detection using the proposed method is significantly better than the approach by integrating ICA and ANN. Furthermore, the prediction output can be used to identify different types and levels of structure damages.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kiremidjian, A.S., Straser, E.G., Law, K.H., Sohn, H., Meng, T., Redlefsen, L., Cruz, R.: Structural Damage Monitoring for Civil Structures. In: International Workshop on Structural Health Monitoring. Stanford University, Stanford, CA, USA, September 1997, pp. 18–20 (1997)
Zang, C.: MI Friswell, M Imregun, Structural Damage Detection using Independent Component Analys. Structural Health Monitoring, An Int. J. 3(1), 69–84 (2004)
DeCoste, D., Schölkopf, B.: Training invariant support vector machines. Machine Learning (2001)
Doebling, S.W., Farrar, C.R., Prime, M.B., Shevitz, D.W.: Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: a Literature Review. The Shock and Vibration Digest. Los Alamos National Laboratory, vol. 30 (2), pp. 91–105 (1998)
Fritzen, C.-P., Jennewein, D., Kierer, T.: Damage Detection Based on Model Updating Methd. Mechanical systems and Signal Processing 12(1), 163–186 (1998)
Song, H., Zhong, L., Moon, F.: Structural Damage Detection by Integrating Independent Component Analysis and Artificial Neural Networks. In: Int. Conf. MLMTA (2005) (accepted)
Müller, K., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An Introduction to Kernel- Based Learning Algorithm. IEEE Transactions on Neural Networks 12(2) (2001)
Müller, K.-R., Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.N.: Predicting time series with support vector machines. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)
Rytter, A.: Vibration Based Inspection of Civil Engineering Structures, Ph. D. Dissertation. Department of building Technology and Structural Engineering, Aalborg University, Denmark (1993)
Masri, S.F., Smyth, A.W., Chassiakos, A.G., Caughey, T.K., Hunter, N.F.: Application of Neural Networks for Detection of Changes in Nonlinear System. J. of Engineering Mechanics, 666–676 (July 2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, H., Zhong, L., Han, B. (2005). Structural Damage Detection by Integrating Independent Component Analysis and Support Vector Machine. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_79
Download citation
DOI: https://doi.org/10.1007/11527503_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
eBook Packages: Computer ScienceComputer Science (R0)