Heterogeneity: The key to failure forecasting.

Jérémie Vasseur,Fabian B Wadsworth,Andrew F Bell,Yan Lavallée,Donald B Dingwell,Ian G Main

doi:10.1038/srep13259

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.

Highlights

Elastic waves are generated when brittle materials are subjected to increasing strain
Sustained microcrack initiation, multiplication and coalescence often results in a critical density of fractures whereby macroscopic rupture ensues. In this manner fracturing in heterogeneous materials is pervasive prior to failure as cracks propagate small distances between flaws and strain energy can be readily dissipated elastically[4]
The crack propagation distance is relatively large and the strain energy stored must exceed the activation energy required for nucleation and propagation of fractures across the sample[2]

Summary

Methods

Following the procedure described in detail in reference 27, we applied the TROL to catalogues of acoustic events in order to retrospectively forecast failure. This law has three free parameters (k, p and tc) to adjust since they are not known a priori. The ML method has been shown to provide statistically stable and repeatable estimates of these parameters[27] This method uses the timings of individual AE events rather than event rates determined in spaced bins (as is commonly the case when applying the standard FFM). Uncertainties on the fitted parameters require prior constraint to be reliably computed such that this precludes the estimation of meaningful error bars on the forecasted failure times

Author Contributions

Findings

Additional Information