Year: 2024
Type: article
Abstract: Fracture networks in rocks and other geomaterials form by seismic and aseismic damage processes that could lead to system‐size failure. Here, we use a multi‐view convolutional neural network model to predict the stress proximity to macroscopic failure of experimentally deformed rock samples using two‐dimensional images. Models are trained on time series of fractures observed in rock samples through dynamic in situ synchrotron X‐ray tomography experiments. The results demonstrate that deep learning models outperform traditional estimates based on fracture density, increasing the accuracy of the predictions. Furthermore, the models provide insights into fundamental characteristics of fracture patterns that may provide precursory information on impending material failure. The trained deep learning models estimate the angle of the fracture plane relative to the principal loading direction, which is a key factor contributing to shear failure. The predicted angle of the fracture plane, in the range of 10°–30° with respect to the direction of maximum compressive stress, is consistent with the established failure criteria used in rock mechanics.
DOI: 10.1029/2023jh000103
Institutions: University of Copenhagen, University of Oslo, European Synchrotron Radiation Facility, University of Southern California, Statewide California Earthquake Center, Centre National de la Recherche Scientifique, Institut des Sciences de la Terre, Institut de Recherche pour le Développement, Université Grenoble Alpes
Language: English
Topic: Rock Mechanics and Modeling
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4402005117