分类： 矿山工程技术 >> 矿山工程技术其他学科 提交时间： 2024-07-08
摘要： Rockburst is a phenomenon where sudden, catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation process. Rockburst disasters endanger the safety of people’s lives and property, national energy security, and social interests, so it is 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 attempted to summarize the application of AI techniques in rockburst prediction. However, these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction, or they do not provide a comprehensive overview. 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 summarized, alongside forecasting future research trends to address existing challenges, while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.
分类： 矿山工程技术 >> 矿山工程技术其他学科 提交时间： 2024-07-08
摘要： The key to achieving rockburst warning lies in the understanding of rockburst precursors. Considering the correlation characteristics of rockburst acoustic emission (AE) parameters, a self-organizing map neural network (SOMNN) based method for rockburst precursor inversion was proposed. The feature of this method lies in acyclic data segmentation iteration process based on the thinking of "interference signal screening", "key signal extraction", and "precursor signal inversion". The rationality of this method has been verified in three groups of rockburst experiments. The results revealed that rockburst AE precursor signals consist of a series of signals characterized by long duration, high energy, low average frequency, high energy amplitude, and low peak frequency. Subsequently, potential value in long term rockburst warning of the precursor obtained in this study was shown via the comparison of conventional precursors. Finally, a preliminary interpretation for rockburst precursor was proposed under the framework of AE parameters physical significance, and it is revealed that AE precursor signals are likely linked to the creation of large-scale tensile cracks before rockburst
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