Community detection forecasts material failure in a sheared granular material

TL;DR

This study uses community detection in contact force networks of sheared granular materials to forecast material failure, revealing that increasing community volatility signals imminent failure.

Contribution

It introduces a novel network-based approach with community detection to predict failure in granular materials, surpassing force-based methods.

Findings

Community structure volatility increases before failure.

Both weak and strong forces contribute to failure forecasting.

Community detection provides earlier failure predictions.

Abstract

The stability of a granular material is a collective phenomenon controlled by individual particles through their interactions. Forecasting when granular materials will undergo an abrupt failure is an ongoing challenge due to the intricate interactions between particles. Here, we report experiments on photoelastic disks undergoing intermittent stick-slip dynamics in a quasi-2D annular shear apparatus, with the evolving network of contact forces made visible via polarized light. We characterize the system by interpreting the interparticle forces as a multilayer network, and apply GenLouvin community detection to identify strongly correlated groups of particles. We observe that the community structure becomes increasingly volatile as the material approaches failure, and that this volatility provides a forecast that precedes what is detectable by considering the forces alone. We additionally observe that both weak and strong forces contribute to the strength of this forecast. These findings provide a new approach to detect patterns of causality and forecast impending failures.