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22 April 1996 Relative performance evaluation of pattern recognition models for nondestructive damage detection
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Abstract
The objective of this paper is to evaluate the relative performance of several Bayesian distance-based pattern recognition models and two non-Bayesian models for non-destructive damage detection (NDD). A theory of damage localization, which yields information on the location of the damage directly from changes in mode shapes, is formulated. Next, the application of pattern recognition for NDD is established. Expressions for pattern classification using discriminate functions based on Bayes' Rule, Neyman-Pearson criteria, and neural networks are generated. A set of criteria for the evaluation of the pattern recognition models is then established. Damage localization is applied to a finite element mode of a structure which contains simulated damage at various locations using the pattern recognition and neural network models. Finally, the evaluation of the pattern recognition models is carried out using the established criteria.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gabriel V. Garcia, Norris Stubbs, and Karen Butler "Relative performance evaluation of pattern recognition models for nondestructive damage detection", Proc. SPIE 2719, Smart Structures and Materials 1996: Smart Systems for Bridges, Structures, and Highways, (22 April 1996); https://doi.org/10.1117/12.238846
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