The assessment of the stope stability is of great importance in the underground mine design process. The instability of stopes leads to serious economic and safety problems in mining operations. This paper aims to assist in the prediction of stope stability by making use of machine learning algorithms. For this purpose, a hybrid grid search-based Artificial Neural Network method is proposed. In the experiments, a total of 215 stope cases which have been collected from six underground mining operations located across Australia are used to prove the validity of the proposed method. Then, the performance of the proposed method is compared with k-nearest neighbour (kNN), naive Bayes (NB), support vector machine (SVM), decision tree (DT) and the stability graph method. Furthermore, several performance measures, such as accuracy, precision, specificity, recall, f-measure, and g-mean are considered during the performance comparison of the methods. The accuracy values are obtained as 91.63%, 81.86%, 77.21%, 76.74%, 73.95%, and 69.77% with the proposed hybrid grid search-based ANN method, SVM, DT, NB, kNN, and the stability graph method, respectively. The empirical results from the experiments indicated that the proposed hybrid approach outperformed the other aforementioned methods, which confirms that the proposed method is a useful tool to predict stope stability.

Deeper deposits become the future of mining as an increasing amount of shallow deposits are exploited. Consequently, the mining industry is being forced to extract minerals from deeper deposits using underground mining methods. This increased the demand for underground mine planning and optimization techniques. Stope boundary optimization is an important step of underground mine design and planning due to its direct impact on the economics of the project. It focuses on maximising the economic profit by selecting the best possible layout; by considering operational, geotechnical and physical constraints. There are a number of algorithms that have been developed for stope boundary optimization for the last two decades. In this paper, an existing underground mine is examined where sublevel stoping mining method is applied. Four algorithms are applied to this underground mine to investigate their applicability in underground mine planning and design process. The selected algorithms are Floating Stope, Maximum Value Neighbourhood, and two special applications which are developed by Sens and Topal and Sandanayake and Topal. The capabilities and limitations of these algorithms are evaluated and compared to each other. Then, they are compared with the stope optimization results from an existing underground mine.

Ground vibration is one of the most dangerous environmental problems associated with blasting operations in mining. Therefore, accurate prediction and controlling the blast-induced ground vibration are imperative for environmental protection and sustainable development. The empirical approaches give inaccurate results, as evident in the literature. Hence, numerous researchers have started to use fast-growing soft computing approaches that are satisfying in prediction performance. However, achieving high-prediction performance and detecting prediction uncertainty is crucial, especially in blasting operations. This study aims to propose a deep ensemble model to predict the blast-induced ground vibration and quantify the prediction uncertainty, which is usually not addressed. This study used 200 published data from ten granite quarry sites in Ibadan and Abeokuta areas, Nigeria. The empirical equation (United States Bureau of Mines-based approach) was applied for comparison. The comparison of the models demonstrated that the proposed deep ensemble model achieved superior performance, offering more accurate predictions and more reliable uncertainty quantification. Specifically, it exhibited the lowest root mean square error (22.674), negative log-likelihood (4.44), and mean prediction interval width (1.769), alongside the highest $R^{2 }$value (0.77) and prediction interval coverage probability (0.95). The deep ensemble model reached the desired coverage of 95%, demonstrating that uncertainty was not underestimated or overestimated.

Machine learning (ML) models provide useful tools to generate spatial estimations of geological features, but they do not consider the spatial dependence among the observations and they primarily use coordinates as predictors. Thus, many ML models produce visible artifacts in the resulting estimates along the coordinate directions. To overcome this significant problem, this paper presents an ensemble super learner (ESL) model which uses the super learner (SL) model as the ML model. In the ESL model, numerous training sets are created from the original dataset by a coordinate rotation strategy and then the estimates obtained from the fitted SL models are ensembled to produce a final estimate. A dataset from a high-grade gold deposit demonstrates the approach and compares the results to kriging and the SL model. The results demonstrate that the ESL model manages artifacts in ML spatial estimation. It also provides better results than the kriging and SL model in terms of estimation accuracy.