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Proceedings Paper

Application of pattern recognition techniques to identify structural change in a laboratory specimen
Author(s): Mustafa Gul; F. Necati Catbas; Michael Georgiopoulos
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Paper Abstract

Identification of damage in a structure, or structural change in general, has been a challenging problem for the researchers in Structural Health Monitoring (SHM) area. Over the last a few decades, a number of experimental and analytical techniques have been developed and used to solve such problem. It has been has been recently accepted in the literature that the process of damage identification problem is one where statistical pattern recognition techniques can be of use because of the inherent uncertainties of the problem. Time series analysis is one of the methods, which is implemented in statistical pattern recognition applications to SHM. In previous studies, Auto-Regressive (AR) models are highly utilized for this purpose. In this study, AR model coefficients are used with different outlier detection and clustering algorithms to detect the change in the boundary conditions of a steel beam. A number of different boundary conditions are realized by using different types and amounts of elastomeric pads. The advantages and the shortcomings of the methodology are discussed in detail based on the experimental results in terms of the ability of it to detect the structural changes and localize them.

Paper Details

Date Published: 10 April 2007
PDF: 10 pages
Proc. SPIE 6529, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007, 65291N (10 April 2007); doi: 10.1117/12.717155
Show Author Affiliations
Mustafa Gul, Univ. of Central Florida (United States)
F. Necati Catbas, Univ. of Central Florida (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 6529:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007
Masayoshi Tomizuka; Chung-Bang Yun; Victor Giurgiutiu, Editor(s)

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