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Proceedings Paper

Nonlinear structural damage detection using support vector machines
Author(s): Li Xiao; Wenzhong Qu
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Paper Abstract

An actual structure including connections and interfaces may exist nonlinear. Because of many complicated problems about nonlinear structural health monitoring (SHM), relatively little progress have been made in this aspect. Statistical pattern recognition techniques have been demonstrated to be competitive with other methods when applied to real engineering datasets. When a structure existing 'breathing' cracks that open and close under operational loading may cause a linear structural system to respond to its operational and environmental loads in a nonlinear manner nonlinear. In this paper, a vibration-based structural health monitoring when the structure exists cracks is investigated with autoregressive support vector machine (AR-SVM). Vibration experiments are carried out with a model frame. Time-series data in different cases such as: initial linear structure; linear structure with mass changed; nonlinear structure; nonlinear structure with mass changed are acquired.AR model of acceleration time-series is established, and different kernel function types and corresponding parameters are chosen and compared, which can more accurate, more effectively locate the damage. Different cases damaged states and different damage positions have been recognized successfully. AR-SVM method for the insufficient training samples is proved to be practical and efficient on structure nonlinear damage detection.

Paper Details

Date Published: 19 April 2012
PDF: 8 pages
Proc. SPIE 8348, Health Monitoring of Structural and Biological Systems 2012, 83482U (19 April 2012); doi: 10.1117/12.914688
Show Author Affiliations
Li Xiao, Wuhan Univ. (China)
Wenzhong Qu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 8348:
Health Monitoring of Structural and Biological Systems 2012
Tribikram Kundu, Editor(s)

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