For better stability, safety and water resource management in a dam, it is important to evaluate the amount of seepage from the dam body. This research is focused on machine learning approach to predict the amount of seepage from Pakistan’s Earth and rock fill Tarbela Dam during 2003 to 2015. The data of temperature, rainfall, water inflow, sediment inflow, reservoir level collected during 2003 to 2015 served as input while the seepage from dam during this period was the output. Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB), have been used to model the input-output relationship. The algorithms used to predict the dam seepage reported a high R² scores between actual and predicted values of average seepage, suggesting their reliability in predicting the seepage in the Tarbela Dam. Moreover, the CatBoost algorithm outperformed, by achieving an R² score of 0.978 in training, 0.805 in validation, and 0.773 in testing phase. Similarly, RMSE was 0.025 in training, 0.076 in validation, and 0.111 in testing phase. Furthermore, to understand the sensitivity of each parameter on the output (average seepage), Shapley Additive Explanations (SHAP), a model explanation algorithm, was used to understand the affect of each parameter on the output. A comparison of SHAP used for all the machine learning models is also presented. According to SHAP summary plots, reservoir level was reported as the most significant parameter, affecting the average seepage in Tarbela Dam. Moreover, a direct relationship was observed between reservoir level and average seepage. It was concluded that the machine learning models are reliable in predicting and understanding the dam seepage in the Tarbela Dam. These Machine Learning models address the limitations of humans in data collecting and analysis which is highly prone to errors, hence arriving at misleading information that can lead to dam failure.

Clustering approaches are widely used to group similar objects and facilitate problem analysis and decision-making in many fields. During short-term planning of open-pit mines, clustering aims to aggregate similar blocks based on their attributes (e.g., geochemical grades, rock types, geometallurgical parameters) while honoring various constraints: i.e., cluster shapes, size, alignment with mining direction, destination, and rock type homogeneity. This approach helps to reduce the computational cost of optimizing short-term mine plans. Previous studies have presented ways to perform clustering without honoring constraints specific to mining. This paper presents a novel block clustering heuristic capable of considering and honoring a set of mining block aggregation requirements and constraints. Constraints can relate to the clustering adjacent blocks, achieving higher destination homogeneities, controlled cluster size, consistency with mining direction, and achieving clusters with mineable shapes and rock types’ homogeneity. The proposed algorithm’s application on two different datasets demonstrates its efficiency and capability in generating reasonable block clusters while meeting different predefined aggregation requirements and constraints.

In rock mechanics, the dilatancy point is always occurring before rock failure during loading process. Water content plays a significant role in the rock physiomechanical properties, which also impact the rock dilatancy point under loading process. This dilatancy point significantly plays a warning role in the rock engineering structures stability. Therefore, it is essential to predict the rock dilatancy point under different water contents to get an early warning for effective monitoring of engineering projects. This study investigates the water contents effects on sandstone dilatancy point under loading in the presence of infrared radiation (IR). Furthermore, this IR was used for the first time as an input parameter for different artificial intelligence (AI) techniques to predict the dilatancy point in the stress-strain curve. The experimental findings show that the stress range in stress-strain curve stages (crack closure and unstable crack propagation) increases with water content. However, this range for deformation and stable crack propagation stages decreases with water content. The dilatancy stress, crack initiation stress, and elastic modulus are negatively linearly correlated, while peak stress and stress level are negatively quadraticaly correlated with a high (⁠ $R$²). The absolute strain energy rate, which gives a sudden increase at the point of dilatancy, is used as the dilatancy point index. The stress level is 0.86 σ_max at the dilatancy point for dry rock and decreases with water content. This index is predicted from IR data using three computing techniques: artificial neural network (ANN), random forest regression (RFR), and k-nearest neighbor (KNN). The performance of all techniques was evaluated using $R$² and root-means-square error (RMSE). The results of the predicted models show satisfactory performances for all, but KNN is remarkable. The research findings will be helpful and provide guidelines about underground engineering project stability evaluation in water environments.

Coal dust explosion constitutes a significant hazard in underground coal mines, coal power plants and other industries utilising coal as fuel. Knowledge of the explosion mechanism and the factors causing coal explosions is essential to investigate for the identification of the controlling factors for preventing coal dust explosions and improving safety conditions. However, the underlying mechanism involved in coal dust explosions is rarely studied under Artificial Intelligence (AI) based modelling. Coal from three different regions of Khyber Pakhtunkhwa, Pakistan, was tested for explosibility in 1.2 L Hartmann apparatus under various particle sizes and dust concentrations. First, a random forest algorithm was used to model the relationship between inputs (coal dust particle size, coal concentration and gross calorific value (GCV)), outputs (maximum pressure (P_max) and the deflagration index (K_st)). The model reported an R² value of 0.75 and 0.89 for P_max and K_st. To further understand the impact of each feature causing explosibility, the random forest AI model was further analysed for sensitivity analysis by SHAP (Shapley Additive exPlanations). The study revealed that the most critical parameter affecting the explosibility of coal dust were particle size > GCV > concentration for P_max and GCV > Particle size > Concentration for Kst. Mutual interaction SHAP plots of two variables at a time revealed that with 400 gm/L), explosibility is relatively lower irrespective of GCV and particle sizes.