Appraisal of Machine Learning Approach to Drifts and Stopes Damage Monitoring in Underground Mines

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.