Applications of Machine Learning Models to Predict the Uniaxial Compressive Strength of Sandstone from Muzaffarabad - A Case Study

Optimizing Machine Learning Models for Predicting Rock Cohesion and Angle of Internal Friction: A Comparative Study of Lithological Analysis, Robustness Assessment, and SHAP Explanations

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.

A data-driven framework for predicting mechanical properties of coal–rock composites in deep mining

Comparative Evaluation of Intelligent Machine Learning Models for Prediction of Uniaxial Compressive Strength of Rocks

The results showed that all models successfully captured the relationship between the input parameters and uniaxial compressive strength, although their predictive capabilities differed considerably, and KStar produced the most accurate predictions.

Tree Based Machine Learning Framework for Stability Evaluation of Square Tunnels in Sloping Rock

The proposed AFELA–machine learning framework provides a computationally efficient, reliable, and interpretable approach for tunnel stability assessment in sloping rock mass conditions.

Machine Learning-Based Prediction of Compressive Strength in Basalt Fiber Reinforced Concrete

Accurate prediction of the mechanical strength of Basalt Fiber Reinforced Concrete (BFRC) is critical for structural design, safety assessment, and the advancement of sustainable infrastructure in civil engineering. Traditional prediction methods often fail to capture the nonlinear relationships between BFRC mix proportions and resulting strength characteristics, leading to unreliable estimations. To address this limitation, this study proposes the Optimized Moment Balanced Machine (OMBM), an advanced machine learning model developed to improve the predictive accuracy of BFRC strength parameters. The model was trained and evaluated using key input features, including cement content, silica fume, fly ash, superplasticizer, water, aggregate composition, and fiber property parameters. The performance of the OMBM was benchmarked against four established machine learning models, such as Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Linear Regression (LR). Results from ten-fold cross-validation show that OMBM consistently outperforms the comparison models across five evaluation metrics. It achieved the lowest RMSE (2.411), MAE (1.788), and MAPE (4.08%), along with the highest values for correlation coefficient (R = 0.978), and coefficient of determination (R2 = 0.956). Furthermore, the OMBM achieved a Reference Index (RI) score of 1.000, which confirms its position as the leading predictive model within this comparative framework. These results confirm the robustness and reliability of the proposed OMBM model, making it a highly effective tool for accurate strength prediction of BFRC. This approach offers significant potential for the advancement of sustainable infrastructure by enabling more accurate and efficient use of concrete materials.

Interpretable Machine Learning for Mechanical Property Prediction of 5Cr-0.5Mo Steel: SHAP Explainability, Multi-Model Comparison, and Uncertainty Quantification

5Cr-0.5Mo ferritic steels are widely used in high-temperature power-plant components. Although artificial neural network (ANN) models have shown good performance in predicting tensile properties, they provide limited insight into predictions and generally do not quantify the uncertainty. In this study, three tree-based machine learning models—Random Forest (RF), XGBoost (XGB), and Gradient Boosting (GB)—were developed using 36 unique alloy grade–temperature observations from a validated NIMS 5Cr-0.5Mo tensile dataset. The model performance was evaluated using leave-one-grade-out (LOGO) cross-validation, with pooled out-of-fold (OOF) predictions used to assess the overall performance. SHapley Additive exPlanations (SHAP) were used to examine feature contributions, whereas Gaussian Process Regression (GPR) was evaluated as a proof-of-concept for uncertainty quantification of yield strength (YS). GB showed the strongest performance for ultimate tensile strength (UTS) and reduction in area (RA), achieving pooled OOF R2 values of 0.9698 and 0.9570, respectively. RF achieved corresponding R2 values of 0.9406 and 0.9488, respectively. SHAP identified the test temperature as the most influential feature across all four properties, whereas the Cr content and austenite grain size contributed significantly to the strength predictions. For YS, the GPR achieved complete empirical coverage of the 95% predictive intervals, although the relatively large mean interval width indicated conservative uncertainty estimates. Given the limited dataset and feature correlations, the SHAP results should be regarded as exploratory, rather than mechanistic. Overall, this study demonstrates the potential of interpretable, uncertainty-aware ML for small alloy datasets, while emphasizing the need for larger, compositionally diverse datasets and independent validation.