Jérémie Vasseur
Heterogeneity: The key to failure forecasting
Vasseur, Jérémie; Wadsworth, Fabian B.; Lavallée, Yan; Bell, Andrew F.; Main, Ian G.; Dingwell, Donald B.
Authors
Dr Fabian Wadsworth fabian.b.wadsworth@durham.ac.uk
Associate Professor
Yan Lavallée
Andrew F. Bell
Ian G. Main
Donald B. Dingwell
Abstract
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.
Citation
Vasseur, J., Wadsworth, F. B., Lavallée, Y., Bell, A. F., Main, I. G., & Dingwell, D. B. (2015). Heterogeneity: The key to failure forecasting. Scientific Reports, 5(1), Article 13259. https://doi.org/10.1038/srep13259
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 23, 2015 |
Online Publication Date | Aug 26, 2015 |
Publication Date | Aug 26, 2015 |
Deposit Date | Mar 20, 2018 |
Publicly Available Date | May 30, 2018 |
Journal | Scientific Reports |
Publisher | Nature Research |
Peer Reviewed | Peer Reviewed |
Volume | 5 |
Issue | 1 |
Article Number | 13259 |
DOI | https://doi.org/10.1038/srep13259 |
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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/
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