期刊
APPLIED SOFT COMPUTING
卷 36, 期 -, 页码 368-374出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2015.06.057
关键词
Damage detection; Information fusion; Support Vector Machine; DS evidence theory
资金
- Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) [30920140122007]
- Natural Science Foundation of China [61503313, 61202144, 61271337]
An intelligent detection method is proposed in this paper to enrich the study of applying machine learning and data mining techniques to building structural damage identification. The proposed method integrates the multi-sensory data fusion and classifier ensemble to detect the location and extent of the damage. First, the wavelet package analysis is used to transform the original vibration acceleration signal into energy features. Then the posteriori probability support vector machines (PPSVM) and the Dempster-Shafer (DS) evidence theory are combined to identify the damage. Empirical study on a benchmark structure model shows that, compared with popular data mining approaches, the proposed method can provide more accurate and stable detection results. Furthermore, this paper compares the detection performance of the information fusion at different levels. The experimental analysis demonstrates that the proposed method with the fusion at the decision level can make good use of multi-sensory information and is more robust in practice. (C) 2015 Elsevier B.V. All rights reserved.
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