Crack path predictions in heterogeneous media by machine learning

M. Worthington, H. B. Chew

Research output: Contribution to journalArticlepeer-review


The interaction between stress fields at the crack-tip and nearby microstructural heterogeneities influences the crack path, and in turn, the effective fracture toughness of a material. In this paper, we assess the ability of artificial neural networks (ANNs) for machine learning to predict the crack paths from given initial void defect distributions in the material, and to provide insights into the underlying crack growth mechanics. Our ANN accurately captures the process zone size, crack growth sequence, and resulting crack patterns in the simplistic case where the proximity of voids to the crack-tip forms the criterion for crack advance. In a ductile medium, pre-existing voids grow with deformation and link up with the primary crack either contiguously or through the formation of multiple unconnected damage zones. The complex crack patterns for both these ductile fracture processes are successfully captured by an ANN, trained on the cracking sequences in a micromechanics-based ductile fracture model containing two size-scales of voids. In addition, the ANN architecture is capable of predicting stochastic crack growth, by providing a multiplicity of possible crack paths, along with a quantified likelihood of each path. Results further demonstrate the utility of autonomous crack path predictions in enabling fracture-by-design.

Original languageEnglish (US)
Article number105188
JournalJournal of the Mechanics and Physics of Solids
StatePublished - Mar 2023


  • Artificial neural networks
  • Crack patterns
  • Micromechanics model
  • Stochastic crack growth
  • Voids

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering


Dive into the research topics of 'Crack path predictions in heterogeneous media by machine learning'. Together they form a unique fingerprint.

Cite this