Mechanisms of extensive fracture propagation post-coalescence: A machine learning assisted discovery

This study investigates the spatial arrangement of existing crack pathways and the crack network at the time of crack coalescence in brittle rock-like materials, focusing on the factors that contribute to rapid and extensive crack growth immediately afterward. To that end, we extract relevant informative features from simulated crack networks generated by the HOSS simulator and employ machine learning methods to establish correlations between these features and the initiation of rapid and extensive crack propagation following coalescence. These features serve as indicators revealing the underlying mechanisms of crack propagation and coalescence. We utilize a Support Vector Machine (SVM) classifier with an RBF kernel to delineate the decision boundary between samples experiencing rapid and extensive crack propagation post-coalescence and those that do not. Through permutation feature importance evaluation, we identify the seven most crucial crack-network features associated with rapid and extensive crack growth. Notably, these features are particularly related to the status of energy buildup, energy distribution, and energy release inside the material. This study aims to elucidate the rapid and extensive fracture propagation post-coalescence in terms of the KDE-based and subgraph-based features. The fundamental assumption guiding this study is that direct observation of the stress and energy state within the material is not feasible. Consequently, understanding the phenomena of coalescence and extensive fracture propagation post-coalescence requires an exploration of the mechanisms observed through the spatiotemporal evolution of the crack network under uniaxial compression.