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Heterogeneity: The key to failure forecasting.

Jérémie Vasseur¹, Fabian B Wadsworth¹, Yan Lavallée²

¹Earth and Environmental Sciences, Ludwig Maximilian University, Munich, Germany.

Scientific Reports

|August 27, 2015

Summary

This summary is machine-generated.

Material heterogeneity significantly improves the accuracy of the Failure Forecast Method (FFM). Understanding this link enhances predictions for catastrophic failures in various materials.

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Area of Science:

Geophysics
Material Science
Mechanical Engineering

Background:

Brittle materials generate elastic waves under strain, with non-linear increases in number and energy preceding catastrophic failure.
Geophysical signals like seismicity and deformation can indicate damage accumulation, forming the basis of the Failure Forecast Method (FFM).

Purpose of the Study:

To investigate the influence of microstructural heterogeneity on deformation mechanisms and the accuracy of the FFM.
To determine if material heterogeneity is a critical factor controlling the effectiveness of failure prediction.

Main Methods:

Generation of synthetic material samples with controlled variations in microstructural heterogeneity (gas volume fraction).
Experimental testing of these samples under increasing strain to observe elastic wave generation and deformation patterns.
Analysis of the relationship between material heterogeneity, deformation style, and the predictive accuracy of the FFM.

Main Results:

The accuracy of failure prediction using the FFM increases significantly with greater material heterogeneity.
Microstructural heterogeneity is identified as a primary control on the power and reliability of failure forecasting.
Deformation style and mechanisms are tightly correlated with the degree of material heterogeneity.

Conclusions:

Material heterogeneity is a crucial, previously underappreciated factor in the success of failure forecasting methods.
Accounting for microstructural heterogeneity can substantially improve the reliability and accuracy of predicting catastrophic failures.
These findings have broad implications for disciplines relying on failure prediction, including volcanology, landslide analysis, and materials science.