Extraction of Force-Chain Network Architecture in Granular Materials Using Community Detection

TL;DR

This paper introduces a network-based community detection approach to quantitatively analyze and compare force-chain structures in granular materials, linking microscopic force networks to bulk material properties.

Contribution

It develops a novel community detection method using a geographical null model to identify force chains, enabling quantitative analysis of their architecture across different materials.

Findings

Force chains can be effectively identified using community detection.

Force-chain architecture varies with pressure in different granular systems.

The proposed diagnostics reveal statistical properties of force chains relevant to material stability.

Abstract

Force chains form heterogeneous physical structures that can constrain the mechanical stability and acoustic transmission of granular media. However, despite their relevance for predicting bulk properties of materials, there is no agreement on a quantitative description of force chains. Consequently, it is difficult to compare the force-chain structures in different materials or experimental conditions. To address this challenge, we treat granular materials as spatially-embedded networks in which the nodes (particles) are connected by weighted edges that represent contact forces. We use techniques from community detection, which is a type of clustering, to find sets of closely connected particles. By using a geographical null model that is constrained by the particles' contact network, we extract chain-like structures that are reminiscent of force chains. We propose three diagnostics to measure these chain-like structures, and we demonstrate the utility of these diagnostics for identifying and characterizing classes of force-chain network architectures in various materials. To illustrate our methods, we describe how force-chain architecture depends on pressure for two very different types of packings: (1) ones derived from laboratory experiments and (2) ones derived from idealized, numerically-generated frictionless packings. By resolving individual force chains, we quantify statistical properties of force-chain shape and strength, which are potentially crucial diagnostics of bulk properties (including material stability). These methods facilitate quantitative comparisons between different particulate systems, regardless of whether they are measured experimentally or numerically.