Optimizing Machine Learning Models for Predicting Rock Cohesion and Angle of Internal Friction: A Comparative Study of Lithological Analysis, Robustness Assessment, and SHAP Explanations
TL;DR
A machine learning framework that predicts rock cohesion and angle of internal friction from easily measurable physical properties, enabling rapid and cost-effective estimation without the need for complex laboratory testing is developed.
Abstract
Rock cohesion (c) and angle of internal friction (φ) are core parameters for rock mass stability analysis and engineering design; however, traditional triaxial tests are costly and time-consuming, limiting their availability in preliminary engineering assessments. To address this limitation, this study develops a machine learning framework that predicts these parameters from easily measurable physical properties, enabling rapid and cost-effective estimation without the need for complex laboratory testing. Based on a total of 199 sets of measured data from four rock types (shale, limestone, quartzite, and quartz-mica schist) in the Himalayan region, this study uses P-wave velocity (Vp), density (ρ), uniaxial compressive strength (UCS), and tensile strength (TS) as input variables. It employs four models: Support Vector Regression (SVR), Random Forest (RF), Multi-Layer Perceptron (MLP), and extreme gradient boosting (XGBoost) to predict c and φ. Hyperparameters were tuned using grid search and Bayesian optimization. We compared unified modeling with rock-type-specific modeling, performed interpretability analysis using SHapley Additive exPlanations (SHAP), and tested robustness by introducing Gaussian noise. The results show that XGBoost produced the best predictions at c (test set R2 = 0.9901, RMSE = 0.512 MPa), while the Bayesian-optimized SVR model yielded the best results at φ (R2 = 0.9776, RMSE = 0.744°). Rock-type-specific modeling improved the R2 for limestone at φ by 0.3541; the SHAP contribution for UCS and TS exceeded 70%; Random Forest demonstrated the best noise resistance, with a decrease in R2 of less than 0.04 under 10% noise. In summary, the strategy proposed in this paper allows for the selection of prediction schemes based on data quality and lithological differences, providing a feasible approach for rapidly obtaining rock strength parameters.