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

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.