Structural damage detection by integrating independent component analysis and support vector machine

Authors:
Huazhu Song;Luo Zhong;Bo Han
Affiliations:
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, P.R. China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, P.R. China;Center for Information Science and Technology, Temple University, Philadelphia, PA
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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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.