Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVMReport as inadecuate

Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM - Download this document for free, or read online. Document in PDF available to download.

Shock and Vibration - Volume 2016 2016, Article ID 3989743, 12 pages -

Research Article

Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China

Department of Civil Engineering, National Taiwan University, Taipei, Taiwan

Received 17 November 2015; Accepted 1 March 2016

Academic Editor: Abdollah Shafieezadeh

Copyright © 2016 Lin-sheng Huo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An effective method for the damage detection of skeletal structures which combines the cross correlation function amplitude CCFA with the support vector machine SVM is presented in this paper. The proposed method consists of two stages. Firstly, the data features are extracted from the CCFA, which, calculated from dynamic responses and as a representation of the modal shapes of the structure, changes when damage occurs on the structure. The data features are then input into the SVM with the one-against-one OAO algorithm to classify the damage status of the structure. The simulation data of IASC-ASCE benchmark model and a vibration experiment of truss structure are adopted to verify the feasibility of proposed method. The results show that the proposed method is suitable for the damage identification of skeletal structures with the limited sensors subjected to ambient excitation. As the CCFA based data features are sensitive to damage, the proposed method demonstrates its reliability in the diagnosis of structures with damage, especially for those with minor damage. In addition, the proposed method shows better noise robustness and is more suitable for noisy environments.

Author: Lin-sheng Huo, Xu Li, Yeong-Bin Yang, and Hong-Nan Li

Source: https://www.hindawi.com/


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