Deep learning-based nanoindentation for evaluating the mechanical properties of rock-forming minerals

Yanmin Zhou, Binwei Xia, Sisong Zhang, Lei Zhou, Xingguo Zhang, Xinling Li,
Deep learning-based nanoindentation for evaluating the mechanical properties of rock-forming minerals,
Journal of Rock Mechanics and Geotechnical Engineering,
2025,
,
ISSN 1674-7755,
https://doi.org/10.1016/j.jrmge.2025.04.023.
(https://www.sciencedirect.com/science/article/pii/S1674775525002689)
Abstract: This study proposed a deep learning-based nanoindentation simulation method to address the challenge of obtaining the mechanical parameters of rock-forming minerals and the complexity of regression analysis. This approach enables the accurate assessment of rock-forming minerals' mechanical parameters. A material database of nanoindentation load-depth (P-h) curves was generated using the material point method (MPM) to characterize the mechanical behavior of major rock-forming minerals (quartz, albite, and muscovite) in sandstone. We used Bayesian hyperparameter optimization to determine the optimal hyperparameters for training a deep neural network (DNN). The trained DNN model accurately predicted the material parameters of rock-forming minerals using experimental nanoindentation P-h data. Numerical simulations of the uniaxial compression of heterogeneous sandstones were conducted using the predicted parameters to assess the sandstones’ macro-mechanical characteristics. The research findings provide new insights into the fundamental mechanical behavior of heterogeneous rock materials.
Keywords: Rock-forming mineral; Mechanical property; Deep learning; Nanoindentation; Material point method