Rockburst prediction using artificial intelligence techniques: A review

Rockburst is a phenomenon that occurs during mining when there is sudden, violent failure of rock mass in deep underground areas or regions with high tectonic stress. Rockburst disasters endanger the safety of people's lives and property, national energy security, and social interests, so it’s very important to accurately predict rockburst. Traditional rockburst prediction has not been able to find an effective prediction method, and the study of the rockburst mechanism is facing a dilemma. With the development of artificial intelligence (AI) techniques in recent years, more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst mechanism. In previous research, several scholars have endeavored to encapsulate the utilization of AI techniques in rockburst prediction. However, either none of these studies exclusively focused on AI techniques, or there were deficiencies in the thoroughness of the synopses provided. Drawing on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques, this paper conducts a comprehensive review of rockburst prediction methods leveraging AI techniques. Firstly, pertinent definitions of rockburst and its associated hazards are introduced. Subsequently, the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized, with emphasis placed on the respective advantages and disadvantages of each approach. Finally, the strengths and weaknesses of prediction methods leveraging AI are consolidated, alongside forecasting future research trends to address existing challenges, while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.