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VOŘECHOVSKÝ, M.
Original Title
Incorporation of statistical length scale into Weibull strength theory for composites
Type
journal article - other
Language
English
Original Abstract
In this paper an extension of Weibull theory by the introduction of a statistical length scale is presented. The classical Weibull strength theory is self-similar; a feature that can be illustrated by the fact that the strength dependence on structural size is a power law (a straight line on a double logarithmic graph). Therefore, the theory predicts unlimited strength for extremely small structures. In the paper, it is shown that such a behavior is a direct implication of the assumption that structural elements have independent random strengths. By the introduction of statistical dependence in the form of spatial autocorrelation, the size dependent strength becomes bounded at the small size extreme. The local random strength is phenomenologically modeled as a random field with a certain autocorrelation function. In such a model, the autocorrelation length plays the role of a statistical length scale. The focus is on small failure probabilities and the related probabilistic distributions of the strength of composites. The theoretical part is followed by applications in fiber bundle models, chains of fiber bundle models and the stochastic finite element method in the context of quasibrittle failure.
Keywords
Autocorrelation length, Extreme of strength random field, Fiber bundle model, Chain-of-bundles, Extreme value theory, Statistical length scale
Authors
RIV year
2010
Released
30. 6. 2010
Publisher
Elsevier
ISBN
0263-8223
Periodical
COMPOSITE STRUCTURES
Year of study
2010 (92)
Number
9
State
United Kingdom of Great Britain and Northern Ireland
Pages from
2027
Pages to
2034
Pages count
8
BibTex
@article{BUT50523, author="Miroslav {Vořechovský}", title="Incorporation of statistical length scale into Weibull strength theory for composites", journal="COMPOSITE STRUCTURES", year="2010", volume="2010 (92)", number="9", pages="2027--2034", issn="0263-8223" }