A probabilistic model for fast-to-evaluate 2D crack path prediction in heterogeneous materials
This work addresses the need for efficient crack prediction in materials science, though it appears incremental as it builds on existing simulation methods.
The paper tackles the problem of predicting 2D crack paths in heterogeneous materials like concrete by developing a fast-to-evaluate probabilistic model, which drastically reduces CPU time compared to existing numerical simulations.
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.