The discrete fracture networks (DFN) have become indispensable tools for geomechanical modelling of jointed rock masses. The technology creates a three-dimensional (3D) representation of fracture geometry used in the construction of surface and subsurface engineering projects in mining, civil engineering, and fracturing of the reservoir in the oil and gas industry. The approach depends on the accuracy of the data obtained during site investigation to create models that represent the fracture geometry of the structure. The better the acquired information available, the better the stochastic analysis that determines the engineering applications and designs that can be carried out. Therefore, it is important to use instruments that can capture fracture distribution characteristics such as fracture intensity, fracture orientation, spatial distribution, fracture length, fracture aperture, and size. This study provides a detailed review of the recent advances in the application of a DFN for modelling jointed rock masses in different engineering applications. The paper shows the principles of modelling in a DFN, including various data-capturing methodologies, and the general application of DFN in various fields. Several case studies where the DFN method was applied are presented in the paper. These include evaluation of slope in an open pit mine, modelling of discontinuity in tunneling, stability evaluation of coal seam longwall, the design of high-level radioactive waste, prediction of groundwater flow, fracturing of petroleum reservoirs, and geothermal cracking of shale gas in the coal bed. However, despite the versatility of the DFN technique, there are still some limitations and challenges to the integration of complexities encountered in rock masses within DFN models.

Automation is increasingly gaining attention as the global industry moves toward intelligent, unmanned approaches to perform hazardous tasks. Although the integration of autonomous technologies has revolutionized various industries for decades, the mining sector has only recently started to harness the potential of autonomous technology. Lately, the mining industry has been transforming by implementing automated systems to shape the future of mining and minimize human involvement in the process. Automated systems such as robotics, artificial intelligence (AI), the Industrial Internet of Things (IIOT), and data analytics have contributed immensely towards ensuring improved productivity and safety and promoting sustainable mineral industry. Despite the substantial benefits and promising potential of automation in the mining sector, its adoption faces challenges due to concerns about human–machine interaction. This paper extensively reviews the current trends, attempts, and trials in converting traditional mining machines to automated systems with no or less human involvement. It also delves into the application of AI in mining operations from the exploration phase to the processing stage. To advance the knowledge base in this domain, the study describes the method used to develop the human–machine interface (HMI) that controls and monitors the activity of a six-degrees-of-freedom robotic arm, a roof bolter machine, and the status of the automated machine. The notable findings in this study draw attention to the critical roles of humans in automated mining operations. This study shows that human operators are still relevant and must control, operate, and maintain these innovative technologies in mining operations. Thus, establishing an effective interaction between human operators and machines can promote the acceptability and implementation of autonomous technologies in mineral extraction processes.

The quest to address the escalating demand for minerals, driven by technological advancements across various sectors, has necessitated the expansion of deep underground mining operations. As mines extend deeper, the safety risks associated with such environments have become increasingly critical. To mitigate these risks, a comprehensive assessment of underground mine workings is essential to evaluate potential damage and prevent failures in access drifts and stopes. However, the use of conventional ground monitoring instrumentation for deformation data acquisition and subsequent analysis has often proved insufficient for anticipating hazards, especially in complex geological formations within deep underground mines. Recent advancements in big data structuring and the development of intelligent algorithms have significantly improved the ability to address complex challenges, particularly in managing underground space infrastructure. Machine learning (ML) methods such as supervised learning, reinforcement learning, and unsupervised techniques have proven to be powerful tools for monitoring and damage detection in underground environments. This paper provides a focused review of ML applications for the evaluation and monitoring of damage in underground drifts and stopes, highlighting the impact of these technologies on enhancing safety and operational efficiency in deep mining operations. These techniques enable real-time data processing, pattern recognition, and predictive analytics, offering new insights into structural integrity and early warning systems for potential failures. The outcomes derived from the application of various ML techniques indicate that, in some cases, these methods may yield results that are comparable to, or even surpass, those obtained from well-established empirical approaches.