English

A probabilistic model for fast-to-evaluate 2D crack path prediction in heterogeneous materials

Machine Learning 2022-01-07 v2

Abstract

This paper is devoted to the construction of a new fast-to-evaluate model for the prediction of 2D crack paths in concrete-like microstructures. The model generates piecewise linear cracks paths with segmentation points selected using a Markov chain model. The Markov chain kernel involves local indicators of mechanical interest and its parameters are learnt from numerical full-field 2D simulations of craking using a cohesive-volumetric finite element solver called XPER. The resulting model exhibits a drastic improvement of CPU time in comparison to simulations from XPER.

Keywords

Cite

@article{arxiv.2112.13578,
  title  = {A probabilistic model for fast-to-evaluate 2D crack path prediction in heterogeneous materials},
  author = {Kathleen Pele and Jean Baccou and Loïc Daridon and Jacques Liandrat and Thibaut Le Gouic and Yann Monerie and Frédéric Péralès},
  journal= {arXiv preprint arXiv:2112.13578},
  year   = {2022}
}
R2 v1 2026-06-24T08:32:19.951Z