Structural damage detection based on posteriori probability support vector machine and Dempster–Shafer evidence theory

Abstract

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.