Dynamic failure predictions using machine learning and geochemical data - ME Feature Article

Dynamic failures, or “bumps,” are a category of ground failure occurring in underground coal mines that involve the sudden expulsion of coal and rock debris into a working area of an active mine. They are an imperative safety concern for the global coal industry. Dynamic failure events have occurred in nearly every coal mining nation of the world, and there have been an estimated greater than 30,000 fatalities as the result of dynamic failure events globally (Bodziony and Lama, 1996; Kidybinski, 2011). Kidybinski (2011) describes them as “the most dangerous natural phenomenon creating a threat to life and health of miners working in hard coal mines.” In the United States, dynamic failures are significantly less common than in many other coal mining countries. However, when they do occur, they result in a proportionately extremely high rate of accidents and fatalities, with more than 60 percent of cases reported to the U.S. Mine Safety and Health Administration (MSHA) resulting in worker injury up to and including death (MSHA, n.d.). The driving mechanisms behind these events are not yet fully understood. Research aimed at optimizing pillar design and mining practices has resulted in a significant decrease in ground control related fatalities including dynamic failure occurrence over the past three decades (Mark, 2024). However, events continue to occur, and new research focused on engineering controls has failed to yield continued, measurable results. It is likely that to produce a further reduction in the rates of dynamic failure accident occurrence, novel research must be pursued. Recent research performed by the National Institute for Occupational Safety and Health (NIOSH), Spokane Mining Research Division (SMRD) has sought to address this need through a focus on the complex interaction between mining induced stressors and risks innate to the host rock mass through improved incorporation of geologic variables into hazard assessment.