Data-driven fracture mechanics

TL;DR

This paper introduces a novel data-driven approach to brittle fracture mechanics that eliminates traditional material assumptions by integrating variational principles with discrete data points, enabling model-free solutions.

Contribution

It develops a data-driven framework for variational fracture mechanics, providing both local and global minimization formulations that are tested on various fracture scenarios.

Findings

Effective in noisy and noise-free conditions

Applicable to Griffith and R-curve fracture behaviors

Demonstrates flexibility of data-driven fracture modeling

Abstract

We present a new data-driven paradigm for variational brittle fracture mechanics. The fracture-related material modeling assumptions are removed and the governing equations stemming from variational principles are combined with a set of discrete data points, leading to a model-free data-driven method of solution. The solution at a given load step is identified as the point within the data set that best satisfies either the Kuhn-Tucker conditions stemming from the variational fracture problem or global minimization of a suitable energy functional, leading to data-driven counterparts of both the local and the global minimization approaches of variational fracture mechanics. Both formulations are tested on different test configurations with and without noise and for Griffith and R-curve type fracture behavior.