Kathleen Pele ; Jean Baccou ; Loïc Daridon ; Jacques Liandrat ; Thibaut Le Gouic et al. - A probabilistic model for fast-to-evaluate 2D crack path prediction in heterogeneous materials

jtcam:8322 - Journal of Theoretical, Computational and Applied Mechanics, January 12, 2023 - https://doi.org/10.46298/jtcam.8322
A probabilistic model for fast-to-evaluate 2D crack path prediction in heterogeneous materialsArticle

Authors: Kathleen Pele ORCID1,2; Jean Baccou ORCID2,3; Loïc Daridon ORCID2,4; Jacques Liandrat ORCID1,5; Thibaut Le Gouic ORCID1,5; Yann Monerie ORCID2,4; Frédéric Péralès ORCID2,3

  • 1 École Centrale de Marseille [ECM]
  • 2 Laboratoire de micromécanique et intégrité des structures
  • 3 Laboratoire de statistique et des modélisations avancées
  • 4 Mécanique Théorique, Interface, Changements d’Echelles
  • 5 Institut de Mathématiques de Marseille

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 cracking using a cohesive-volumetric finite element solver called XPER. This model does not include any mechanical elements. It is the database, derived from the XPER crack, that contains the mechanical information and optimizes the probabilistic model. The resulting model exhibits a drastic improvement of CPU time in comparison to simulations from XPER.

Published on: January 12, 2023
Accepted on: March 23, 2022
Submitted on: July 30, 2021
Keywords: machine learning,cracking prediction,concrete,Markov chain,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR],[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG],[PHYS.MECA.GEME]Physics [physics]/Mechanics [physics]/Mechanical engineering [physics.class-ph]

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