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Heterogeneity: The key to failure forecasting

Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failur...

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Autores principales: Vasseur, Jérémie, Wadsworth, Fabian B., Lavallée, Yan, Bell, Andrew F., Main, Ian G., Dingwell, Donald B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4549791/
https://www.ncbi.nlm.nih.gov/pubmed/26307196
http://dx.doi.org/10.1038/srep13259
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author Vasseur, Jérémie
Wadsworth, Fabian B.
Lavallée, Yan
Bell, Andrew F.
Main, Ian G.
Dingwell, Donald B.
author_facet Vasseur, Jérémie
Wadsworth, Fabian B.
Lavallée, Yan
Bell, Andrew F.
Main, Ian G.
Dingwell, Donald B.
author_sort Vasseur, Jérémie
collection PubMed
description Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power.
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spelling pubmed-45497912015-09-04 Heterogeneity: The key to failure forecasting Vasseur, Jérémie Wadsworth, Fabian B. Lavallée, Yan Bell, Andrew F. Main, Ian G. Dingwell, Donald B. Sci Rep Article Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power. Nature Publishing Group 2015-08-26 /pmc/articles/PMC4549791/ /pubmed/26307196 http://dx.doi.org/10.1038/srep13259 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Vasseur, Jérémie
Wadsworth, Fabian B.
Lavallée, Yan
Bell, Andrew F.
Main, Ian G.
Dingwell, Donald B.
Heterogeneity: The key to failure forecasting
title Heterogeneity: The key to failure forecasting
title_full Heterogeneity: The key to failure forecasting
title_fullStr Heterogeneity: The key to failure forecasting
title_full_unstemmed Heterogeneity: The key to failure forecasting
title_short Heterogeneity: The key to failure forecasting
title_sort heterogeneity: the key to failure forecasting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4549791/
https://www.ncbi.nlm.nih.gov/pubmed/26307196
http://dx.doi.org/10.1038/srep13259
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