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A Comparative Study of Statistical Outlier Analysis and ANN Methods for Damage Detection and Assessment in Composite Structures
1 RMIT University, School of Aerospace, Bundoora, Victoria, Australia
* To whom correspondence should be addressed. E-mail: sabu.john{at}rmit.edu.au.
This article introduces some of the experimental and analytical work in damage detection applied to polymeric composite T-joints used in maritime structures. Two methods of damage detection are discussed – A statistics-based outlier technique and one using artificial neural networks (ANNs). The SHM using ANNs system was found to be capable of not only detecting the presence of multiple delaminations in a composite structure, but also capable of determining the location and extent of all the delaminations present in the T-joint structure, regardless of the load (angle and magnitude) acting on the structure. The system developed, relies on the examination of the strain distribution of the structure under operational loading. Finally, on testing the SHM system developed with strain signatures of composite T-joint structures, subjected to variable loading, embedded with all possible damage configurations (including multiple damage scenarios), an overall damage (location and extent) prediction accuracy of 88.32% was achieved. These results are presented and discussed in detail in this article.
First published on August 6, 2009 |
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