The University of Massachusetts Amherst
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Random composites characterization using a classifier model

TitleRandom composites characterization using a classifier model
Publication TypeJournal Article
Year of Publication2007
AuthorsLiu H., Arwade SR, Igusa T.
JournalJournal of Engineering Mechanics
Start Page129
Date Published02/2007
KeywordsBayesian analysis, Composite materials, Damage, Decision making, fracture, Microstructures, Statistics, Uncertainty principles

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.