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Abstract This study investigates stress-deformation behavior in Himalayan tunnels, focusing on how geological features impact stability. The objective is to enhance the understanding of displacement phenomena, particularly in tunnels traversing jointed rocks. A modified support system, to specific rock mass classifications, is employed to address the unique challenges posed by geological discontinuities. Kinematic analysis reveals a 20% probability of wedge failure due to these discontinuities. Numerical analysis using Hoek–Brown parameters identifies significant stress concentrations at the tunnel crown, especially in jointed sections, where increased convergence and displacement (1.2 mm at the crown compared to 0.25 mm at the walls) highlight the susceptibility to deformation. The study indicates the critical need for specialized support in jointed regions to mitigate stability risks.

In this study, the dynamics of jointed rock masses in the challenging geological setting of the Himalayas, with a focus on tunneling activities and recommendations of support for jointing rock mass, were investigated. The jointing in Himalayan rocks mass poses significant implications for tunnel stability, demanding a detailed analysis of joint characteristics, such as joint connectivity and spacing. The parameter of joint connectivity rate along the Himalayas becomes a key focus, impacting rock mass strength for tunneling. The study utilized quadratic polynomial and reciprocal expressions of power functions, to characterize the nonlinear variation of jointed rock mass strength concerning joint connectivity rate. Integration of these results leads to unified equations that consider rock type dependence, having a realistic representation of jointed rock behavior. Numerical analysis reveals that jointing in rock affects instability through complex stress regimes, identifying critical stress concentration points. The effectiveness of support measures like rock bolts and shotcrete are validated, demonstrating their role in reducing displacements in tunnels.

Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks. The analysis finds key rock factors that have a substantial impact on the powder factor, hence enabling more accurate planning and execution of blasting operations. The analysis uses data from 180 blast rounds carried out at a dolomite mine in south-south Nigeria. It incorporates measures such as root mean square error (RSME), mean absolute error (MAE), R-squared (R2), and variance accounted for (VAF) to determine the best models for predicting powder factor. The results indicate that the decision tree model (MD4) outperforms alternative approaches, such as artificial neural networks and Gaussian Process Regression (GPR). In addition, the research presents an efficient artificial neural network equation (MD2) for estimating the values of optimum powder factor, demonstrating outstanding blasting fragmentation. In conclusion, this research provides significant information for improving the accuracy of powder factor prediction, which is especially advantageous for small-scale blasting operations.

Abstract The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model to assess the ground vibrations during blasting in mining projects. The long short-term memory (LSTM), artificial neural network (ANN), least square support vector machine (LSSVM), ensemble tree (ET), decision tree (DT), Gaussian process regression (GPR), support vector machine (SVM), and multilinear regression (MLR) models are employed using 162 data points. For the first time, the blackhole-optimized LSTM model has been used to predict the ground vibrations during blasting. Fifteen performance metrics have been implemented to measure the prediction capabilities of computational models. The study concludes that the blackhole optimized-LSTM model PPV11 is highly capable of predicting ground vibration. Model PPV11 has assessed ground vibrations with RMSE = 0.0181 mm/s, MAE = 0.0067 mm/s, R = 0.9951, a20 = 96.88, IOA = 0.9719, IOS = 0.0356 in testing. Furthermore, this study reveals that the prediction accuracy of hybrid models is less affected by multicollinearity because of the optimization algorithm. The external cross-validation and literature validation confirm the prediction capabilities of model PPV11. The ANOVA and Z tests reject the null hypothesis for actual ground vibration, and the Anderson–Darling test rejects the null hypothesis for predicted ground vibration. This study also concludes that the GPR and LSSVM models overfit because of moderate to problematic multicollinearity in assessing ground vibration during blasting.

Blast charge initiation procedures have a significant impact on both mining safety and production rates. In this study, the inventory benefit of an electric initiation system was investigated to assess its influence on both fragmentation and blast-induced damages. The WipFrag software was used to examine the size distribution and productivity of 12 small-scale blasts initiated by both nonelectric and electric detonators. All blast rounds were initiated with plain-type electric and NONEL detonators. The average burden, spacing, stemming length, and charge weight were, respectively, 0.85 m, 1.10 m, 0.66 m, and 1.1 kg. The results showed that the mesh through which 80% of the blast fragments passed for the electric blast was smaller than the mesh through which the material products from the NONEL blast passed. The results also demonstrated that the generated blast-induced ground vibration (PPV) from all blast rounds for electric blast varied from 0.4–1.2 mm/s and 80–105 dB, while that for nonelectric blast ranged from 0.05–0.2 mm/s and 72–95 dB. As a result, the electric blast initiation technique was found to produce good fragmentation, with a higher percentage of optimum fragment sizes on spec than nonelectrically initiated blasts.