Random Composites Characterization Using a Classifier Model
Publication: Journal of Engineering Mechanics
Volume 133, Issue 2
Abstract
A new method is introduced for characterizing and analyzing materials with random heterogeneous microstructure. The method begins with classifiers which process information from high-fidelity analyses of small-sized simulated microstructures. These classifiers are subsequently used in a multipass moving window to identify subregions of potentially critical microscale behavior such as strain concentrations. In the derivation of the method, it is shown how information theory-based concepts can be formulated in a Bayesian decision theory framework that addresses microstructural issues. Furthermore, it is shown how a sequence of classifiers can be constructed to refine the analysis of microstructure. While the method presented herein is general, a relatively simple example of a two-dimensional, two-phase composite is used to illustrate the analysis steps.
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Acknowledgments
The writers wish to acknowledge the support of this work by NSF through grant no. DMI-0423582.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 133 • Issue 2 • February 2007
Pages: 129 - 140
Copyright
© 2007 ASCE.
History
Received: Feb 8, 2005
Accepted: Jul 7, 2006
Published online: Feb 1, 2007
Published in print: Feb 2007
Notes
Note. Associate Editor: Arvid Naess
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