Slope stability assessment is essential for safe and sustainable development widely applied in mining, civil, and environmental engineering projects around the world. This study aimed to conduct a stability analysis of a selected Himalayan road cut slope from two different sections, named sections (A) and (B). The strength reduction factor (SRF) based on the finite element method was used to simulate the slope sections using Phase2 software. A mesh pattern of six node triangle elements was used during the numerical simulation. The Mohr-Coulomb parameters and other inputs used in the numerical modelling of the investigated slope were estimated by different geotechnical tests, namely, the direct shear test, density analysis test, rock hardness test, and Brazilian test. The results indicated that the critical SRF of the completely weathered slope profile section (A), with a relatively low overall angle, was found to be 1.25, which is approximately 50% lower than the value obtained in the moderately to highly weathered profile section (B), equal to 2.53. These results are in agreement with other published studies, which revealed that the geometry of a slope influences the weathering grade, which in turn destabilizes the slope. The results of this study will help in engineering slope design considering the influence of weathering.

Ground vibration induced by rock blasting is an unavoidable effect that may generate severe damages to structures and living communities. Peak particle velocity (PPV) is the key predictor for ground vibration. This study aims to develop a model to predict PPV in opencast mines. Two machine-learning techniques, including multivariate adaptive regression splines (MARS) and classification and regression tree (CART), which are easy to implement by field engineers, were investigated. The models were developed using a record of 1001 real blast-induced ground vibrations, with ten (10) corresponding blasting parameters from 34 opencast mines/quarries from India and Benin. The suitability of one technique over the other was tested by comparing the outcomes with the support vector regression (SVR) algorithm, multiple linear regression, and different empirical predictors using a Taylor diagram. The results showed that the MARS model outperformed other models in this study with lower error (RMSE = 0.227) and R² of 0.951, followed by SVR (R² = 0.87), CART (R² = 0.74) and empirical predictors. Based on the large-scale cases and input variables involved, the developed models should lead to better representative models of high generalization ability. The proposed MARS model can easily be implemented by field engineers for the prediction of blasting vibration with reasonable accuracy.

Accurate lithology classification is essential as variations in rock formations can significantly impact the cost and efficiency of mineral exploration and mining. Initial exploration maps provide insights into subsurface formations, though typically collected at widely spaced intervals. This study examines the use of early exploration data and Measurement While Drilling (MWD) data for lithology prediction through machine learning. The research specifically evaluates the benefit of incorporating spatial coordinates with MWD parameters to enhance classification accuracy, using support vector machine (SVM), random forest (RF), and extra gradient boosting (XGBoost) classifiers with tenfold cross-validation. The models were trained on 235,501 data points of six MWD parameters from 308 drill holes. The effects of raw (imbalanced) versus Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbor (SMOTEENN) (balanced) data were analyzed, along with a comparison between random and spatial data splits. Results indicate that SMOTEENN-balanced data paired with a spatial split strategy consistently improved model stability, with the XGBoost model achieving the highest performance, with a precision of 95.60% and an F1 score of 94.41% on unseen data. Additionally, the study revealed that integrating spatial coordinates of drilling locations consistently enhanced lithology classification, with a notable F1 score improvement of 27.97% using XGBoost. The findings highlight the value of combining spatial coordinates and MWD data for improved lithology classification and offer potential support for geological modeling and sustainable mining practices.

Extracting rock mass strength properties from existing data like Measurement While Drilling (MWD) is important to reduce the cost of additional geological and geotechnical surveys. This study presents an approach that combines clustering (unsupervised learning) and classification algorithms to identify similar rock groups for their prediction. The dataset comprises 272,272 MWD from 2,790 drill holes, split into 215,401 data points (2,332 drill holes) for cross-validation, and another 215,401 data points, from 558 previously unseen drill holes for testing. Principal component analysis (PCA) and clustering algorithms such as K-means, Gaussian mixture, C Fuzy, and hierarchical clustering were employed to group rocks with similar MWD parameters. The combination of PCA and k-means clustering provides good cluster quality which best describes the different rock strength characteristics (clusters), as revealed by geological investigation and coring data. After identifying the rock categories, Extra Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) approaches were used to develop classification models for rock strength prediction. The XGBoost model achieved the best and most reliable performance with accuracy, precision, recall, and F1 score exceeding 98% on the test set. This study highlights the synergetic benefits of combining unsupervised and supervised machine learning techniques to predict rock mass conditions, especially in scenarios with limited geological information or unavailable labeled data.