%0 Journal Article
%T A probabilistic model for fast-to-evaluate 2D crack path prediction in heterogeneous materials
%+ École Centrale de Marseille (ECM)
%+ Laboratoire de micromécanique et intégrité des structures (MIST)
%+ Laboratoire de statistique et des modélisations avancées (IRSN/PSN-RES/SEMIA/LSMA)
%+ Mécanique Théorique, Interface, Changements d’Echelles (MéTICE)
%+ Institut de Mathématiques de Marseille (I2M)
%A Pele, Kathleen
%A Baccou, Jean
%A Daridon, Loïc
%A Liandrat, Jacques
%A Le Gouic, Thibaut
%A Monerie, Yann
%A Péralès, Frédéric
%< avec comité de lecture
%J Journal of Theoretical, Computational and Applied Mechanics
%I INRIA
%V Volume XV
%P 1-21
%8 2023-01-12
%D 2023
%Z 2112.13578
%R 10.46298/jtcam.8322
%K machine learning
%K cracking prediction
%K concrete
%K Markov chain
%Z Mathematics [math]/Probability [math.PR]
%Z Computer Science [cs]/Machine Learning [cs.LG]
%Z Physics [physics]/Mechanics [physics]/Mechanical engineering [physics.class-ph]Journal articles
%X 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.
%G English
%2 https://hal.science/hal-03295926v4/document
%2 https://hal.science/hal-03295926v4/file/JTCAM_8322.pdf
%L hal-03295926
%U https://hal.science/hal-03295926
%~ IRSN
%~ CNRS
%~ UNIV-AMU
%~ EC-MARSEILLE
%~ LMGC
%~ INSMI
%~ I2M
%~ I2M-2014-
%~ MIST
%~ UNIV-MONTPELLIER
%~ UM-2015-2021
%~ UM-EPE
%~ PSNRES
%~ SEMIA
%~ LSMA