Estimation of Rock Shear Strength Parameters from Geophysical Indicators Using an Equilibrium-Optimized Multilayer Perceptron

Abstract

Accurate and scalable estimation of rock shear strength parameters is essential for remote-sensing-supported geological hazard assessment, slope stability evaluation, and engineering geological mapping. However, determining cohesion and internal friction angle requires multiple triaxial tests under different confining pressures, which are time-consuming, costly, and difficult to apply widely. To support remote-sensing-oriented geoscience and civil engineering applications, this study develops a hybrid machine learning framework for estimating cohesion and internal friction angle from geophysical and mechanical indicators. A cross-source database was compiled from published rock records collected from the Jinchuan mining area in China and the Luhri area in India. After completeness screening and unit harmonization, 213 mixed-lithology cases were retained for modeling, with P-wave velocity, density, uniaxial compressive strength, and tensile strength used as input variables. An Equilibrium Optimizer was coupled with a multilayer perceptron to optimize the network weights and biases, and model performance was evaluated using five-fold cross-validation, independent testing, repeated runs, and comparisons with conventional MLP and several typical machine learning models. The proposed EO–MLP model achieved high prediction accuracy, with test-set coefficient of determination values of 0.946 for internal friction angle and 0.983 for cohesion and corresponding RMSE values of 1.072 and 0.671, respectively. Robust scaler normalization produced the best performance among the three tested normalization strategies. SHapley Additive exPlanations analysis indicated that density was the dominant predictor of cohesion, whereas uniaxial compressive strength and P-wave velocity made the largest contributions to internal friction angle prediction. The proposed framework provides an indirect data-driven tool for estimating shear strength parameters and can complement engineering-geological investigation and rock engineering design.