Prediction of triaxial mechanical properties of rocks based on mesoscopic finite element numerical simulation and multi-objective machine learning

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The deformation and strength characteristics of rocks are crucial for the effective development of underground resources and the construction of underground engineering projects. In this study, a novel approach is proposed to predict the triaxial mechanical properties of rocks by utilizing mesoscopic finite element numerical simulation and multi-objective machine learning. First, the mesoscopic mechanical properties of rocks are obtained through microscale finite element simulations to generate a training dataset. Then, three machine learning algorithms, namely support vector regression (SVR), artificial neural network (ANN), and random forest (RF), are employed to perform multi-objective machine learning on the triaxial elastic modulus, Poisson's ratio, and compressive strength of rocks, using uniaxial elastic modulus, Poisson's ratio, tensile strength, compressive strength, and confining pressure as feature variables. The performance evaluation, based on 10-fold cross-validation, demonstrates that all three models exhibit excellent predictive capabilities, especially the SVR and RF models, which show high accuracy and correlation, respectively. Among the five feature variables, confining pressure is the most important feature, while uniaxial tensile strength is the least important feature. The absence of uniaxial tensile strength does not significantly impact the predictive performance of the models. These findings offer novel insights into the investigation of the triaxial mechanical properties of rocks.