Record statistics based prediction of fracture in the random spring network model

TL;DR

This paper investigates how record statistics of damage avalanches in a random spring network can predict material fracture, revealing a maximum in waiting times at moderate disorder and enabling early failure prediction.

Contribution

It introduces a novel record statistics approach to predict fracture in heterogeneous materials, outperforming traditional avalanche-based methods in real-time failure prediction.

Findings

Maximum waiting time occurs at moderate disorder levels.

Record statistics can predict failure at smaller strains.

Linear relation between failure strain and maximum waiting time strain.

Abstract

We study the role of record statistics of damage avalanches in predicting the fracture of a heterogeneous material under tensile loading. The material is modeled using a two-dimensional random spring network where disorder is introduced through randomness in the breakage threshold strains of the springs. It is shown that the waiting time between successive records of avalanches has a maximum for moderate disorder, thus showing an acceleration of records with impending fracture. Such a signature is absent for low disorder strength when the fracture is nucleation-dominated, and high disorder strength when the fracture is percolation type. We examine the correlation between the record with the maximum waiting time and the crossover record at which the avalanche statistics change from off-critical to critical. Compared to the avalanche based predictor for failure, we show that the record statistics have the advantage of both being real-time as well as able to predict final fracture at much smaller strains. We also show that in the avalanche-dominated regime, the failure strain is shown to have a linear relation with the strain at the maximum waiting time, making possible a quantitative prediction.