Abstract
Knowledge transfer from micromechanics of granular media failure to geohazard forecasting and mitigation has been slow. But in the face of a rapidly expanding data infrastructure on the motion of individual grains for laboratory samples – and ground motion data at the field scale – opportunities to accelerate this knowledge transfer are emerging. In particular, such data assets coupled with data-driven approaches enable ‘new eyes’ to re-examine granular failure. To this end, effective strategies that can jump scales from bench to field are urgently needed. Here we demonstrate one strategy that focusses on the study of deformation patterns in the precursory failure regime using kinematic data. Unlike previous studies which focus on regions of high strains, here we probe the development and evolution of near-undeforming regions through the lens of explosive percolation. We find a common dynamical signature in which undeforming regions, which are initially transient in the precursory failure regime, become persistent from the time of imminent failure. We demonstrate the robustness of these findings for data on individual grain motions in a classical laboratory test and ground motion in two real landslides at vastly different scales.
Highlights
Marcel Proust once said “The real voyage of discovery consists not in seeking new landscapes, but in having new eyes” [1]
We cast new eyes on deformation, but we focus on the opposite extreme
Different from [9], here we focus our attention on the undeforming regions, namely groups of points which form connected clusters in both physical space and kinematic space
Summary
Marcel Proust once said “The real voyage of discovery consists not in seeking new landscapes, but in having new eyes” [1]. Past studies of the micromechanics of deformation, aimed toward improving fundamental understanding of the precursory failure regime, have largely focused on the evolution of regions of high strain [2,3,4,5,6,7,8]. We examine the evolution of zones of vanishing deformation in the lead up to, at incipient and during failure – at both laboratory and field levels. The aim is to improve fundamental knowledge of granular failure and help advance data-driven tools for geohazard forecasting and mitigation (e.g., [9,10,11])
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