A hybrid machine learning framework for estimating cohesion and internal friction angle from geophysical and mechanical indicators is developed and provides an indirect data-driven tool for estimating shear strength parameters and can complement engineering-geological investigation and rock engineering design.
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.
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.
This study investigates an artificial intelligence (AI) based approach for real-time prediction of Young's modulus and UCS using drilling and logging data and reveals that neutron porosity, formation bulk density and Gamma Ray are the three most influential predictors.
K. Amadi, R. Elgaddafi, B. M. Bitayib et al.· SPE Nigeria Annual Internati...· 0 citations
Slope stability assessment remains a fundamental challenge in geotechnical engineering because of the complex nonlinear interactions among soil properties, slope geometry, and hydraulic conditions, particularly variations in pore-water pressure. This study investigates the reliability of Artificial Neural Network–Multi-Layer Perceptron (ANN–MLP) models for predicting the Factor of Safety (FoS) of homogeneous soil slopes through a systematic comparison of three gradient-based optimization algorithms: Adam, Mini-Batch Gradient Descent (MBGD), and Nesterov Accelerated Gradient (NAG). A database comprising 2014 slope cases, compiled from published studies and numerically generated using Limit Equilibrium Method (LEM) and Finite Element Method (FEM) analyses, was used for model development and k-fold cross-validation. Beyond statistical evaluation, the developed models were validated using two classical dry-slope benchmark frameworks based on the Taylor stability charts and Bishop–Morgenstern stability coefficients, followed by two documented engineering case studies from Hulu Kelang and Pahang, Malaysia, to assess predictive performance under both dry and variable hydraulic conditions. Adam achieved the highest cross-validated predictive accuracy (R2 = 0.988; RMSE = 0.212), whereas MBGD demonstrated the closest overall agreement with the reference LEM solutions across the validation cases and under increasing pore-water pressure ratios. NAG generally produced more conservative predictions while exhibiting greater sensitivity to hyperparameter selection. All models successfully reproduced the expected nonlinear reduction in FoS with increasing pore-water pressure, consistent with established geotechnical behaviour. The results demonstrate that optimizer selection significantly influences ANN–MLP prediction behaviour and that properly validated gradient-based ANN models can serve as efficient decision-support tools for rapid slope stability assessment under hydraulic variability.
Shaza Soleiman, M. Rahhal· Geotechnics· 0 citations
A machine learning-assisted geophysical–geotechnical framework that integrates Electrical Resistivity Tomography, Seismic Refraction Tomography, and borehole-derived Standard Penetration Test data to improve subsurface characterization and engineering site assessment is presented.
M. Dick, A. Bery, Adedibu Sunny Akingboye et al.· Innovative Infrastructure So...· 4 citations
A reliable rock brittleness index (BI) profile plays a key role in maintaining safe drilling operation and hydraulic fracturing during hydrocarbon exploration and field development. Also, BI is vital for mining projects such as tunneling and rock mass excavation for mineral extraction. Instead of destructive testing measurements for BI, the machine learning (ML) algorithms are widely used to obtain continuous BI profile using real field logging data. The major objectives of this study are a) to evaluate the efficacy of different ML algorithms and b) to find the most contributing geophysical logging parameters for predicting BI of clastic sedimentary formation. The data-driven computational models are developed and optimized using ML techniques of extreme gradient boosting (XGB) and random forest (RF) algorithms along with support vector regression (SVR) using a list of processed 1550 datasets from logging parameters. The explainable AI (XAI) of Shapley Additive Explanations (SHAP) is applied to identify features of importance to ensure transparency and interpretability of the BI model's predictions. Results revealed that XGB outperforms with high determination of coefficient (99%) and least statistical error compared to RF and SVR algorithms to predict BI using logging data of offshore Volve oil field. Based on XGB-based SHAP analysis, it is found that acoustic shear and compressional travel times are most contributing variables for BI forecasting other than deep resistivity, gamma ray, bulk density and neutron porosity. These features importance also verified and confirmed by SHAP value with RF. The applied methodological strategies and research findings from the study could be applied to refine the data-driven model architectures for better performance and reliability in forecasting geomechanical properties and formation evaluation with precise subsurface datasets in petroleum and mining operations.
M. I. Miah, Md. Maruf Alam, T. Wiens· Engineering· 0 citations
