Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

While empirical rock fragmentation models are easy to parameterize for blast design, they are usually prone to errors, resulting in less accurate fragment size prediction. Among other shortfalls, these models may be unable to accurately account for the nonlinear relationship that exists between fragmentation input and output parameters. Machine learning (ML) algorithms are potentially able to better account for the nonlinear relationship. To this end, we assess the potential of the multilayered artificial neural network (ANN) and support vector regression (SVR) ML techniques in rock fragmentation prediction. Using geometric, explosives, and rock parameters, we build ANN and SVR models to predict mean rock fragment size. Both models yield satisfactory results and show higher performance when compared with the conventional Kuznetsov model. We further demonstrate an automated means of analyzing a varied number of hidden layers for an ANN using Bayesian optimization in the Keras Python library.

Underground mines have several hazards that could lead to serious consequences if they come into effect. Acquiring, evaluating, and using the real-time data from the atmospheric monitoring system and miner's positional information is crucial in deciding the best course of action. A graphical user interface–based software is developed that uses an AutoCAD-based mine map, real-time atmospheric monitoring system, and miners' positional information to guide on the shortest route to mine exit and other locations within the mine, including the refuge chamber. Several algorithms are implemented to enhance the visualization of the program and guide the miners through the shortest routes. The information relayed by the sensors and communicated by other personnel are collected, evaluated, and used by the program in proposing the best course of action. The program was evaluated using two case studies involving rescue relating to elevated carbon monoxide levels and increased temperature simulating fire scenarios. The program proposed the shortest path from the miner's current location to the exit of the mine, nearest refuge chamber, and the phone location. The real-time sensor information relayed by all the sensors was collected in a comma-separated value file. This program presents an important tool that aggregates information relayed by sensors to propose the best rescue strategy. The visualization capability of the program allows the operator to observe all the information on a screen and monitor the rescue in real time. This program permits the incorporation of additional sensors and algorithms to further customize the tool.

Identifying the as-drilled location of blastholes is crucial for achieving optimal blasting results. This research proposes a novel integrated methodology to control drilling accuracy in open-pit mines. This approach is developed by combining aerial drone images with machine learning techniques. The study investigates the viability of photogrammetry combined with machine learning techniques, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), for automatically detecting blastholes in photogrammetry representations of blast patterns. To verify the hypothesis that machine learning can detect blastholes in images as effectively as humans, various datasets (drone images) were obtained from different mine sites in Nevada, USA. The images were processed to create photogrammetry mapping of the drill patterns. In this process, thousands of patches were extracted and augmented from the photogrammetry representations. Those patches were then used to train and test different CNN architectures optimized to locate blastholes. After reaching an acceptable level of accuracy during the training process, the model was tested using a piece of completely unknown data (testing dataset). The high recall, precision, and percentage of detected blastholes prove that the combination of SVM, CNN, and photogrammetry (PHG) is an effective methodology for detecting blastholes on photogrammetry maps.

Block caving is an efficient underground mining method for extracting massive and low-grade ore deposits, which are too deep for traditional open-pit mining. However, in the case of orebodies with uranium mineralization, the extraction of broken rock from drawpoints will bring harmful radon gas into working areas. Airborne diesel particulate matter and dust produced from mining activities also contribute to the emissions on the production level. Maintaining relatively negative pressure in the cave by installing exhaust fans is one of the most effective approaches to mitigate gas emission concerns. The selection of proper fan systems is highly related to airflow behavior within the cave. Due to the dynamic caving process and complex cave structure, estimation of cave airflow resistance through field studies is difficult. This study developed a 1:100 scaled experimental and numerical model to investigate the effects of cave parameters (cave porosity, particle size, and undercut structure) on airflow resistance under parallel fan conditions. Results showed that cave airflow resistance increases with decreasing cave porosity and particle size; cave airflow behavior is significantly affected by undercut drift closure and the use of additional fans. This study provides valuable information to optimize ventilation system design in block cave mines.