Search

Rockbursts frequently occur in tunneling projects and pose a serious threat to workers and the environment. Therefore, accurate prediction of rockbursts is of great practical significance. Currently, various rockburst prediction methods exist, with static and dynamic indicators playing a key role. This paper analyzes the importance of rockburst prediction methods based on Citespace software. The results indicate that microseismic monitoring, acoustic emission, and machine learning are the most important methods. The paper focuses on four common rockburst prediction methods: empirical methods, microseismic monitoring, acoustic emission, and machine learning, from the perspective of static and dynamic indicators. The performance and application of static and dynamic indicators in the four common prediction methods in recent years are summarized, the limitations of static and dynamic indicators at this stage are discussed, and possible future development directions are proposed. This paper provides the necessary perspective and tools for better understanding the advantages and disadvantages of static and dynamic indicators in the four rockburst prediction methods. [ABSTRACT FROM AUTHOR]

Technical challenges have always been part of underground mining activities, however, some of these challenges grow in complexity as mining occurs in deeper and deeper settings. One such challenge is rock mass stability and the risk of rockburst events. To overcome these challenges, and to limit the risks and impacts of events such as rockbursts, advanced solutions must be developed and best practices implemented. Rockbursts are common in underground mines and substantially threaten the safety of personnel and equipment, and can cause major disruptions in mine development and operations. Rockbursts consist of violent wall rock failures associated with high energy rock projections in response to the instantaneous stress release in rock mass under high strain conditions. Therefore, it is necessary to develop a good understanding of the conditions and mechanisms leading to a rockburst, and to improve risk assessment methods. The capacity to properly estimate the risks of rockburst occurrence is essential in underground operations. However, a limited number of studies have examined and compared yet different empirical methods of rockburst. The current understanding of this important hazard in the mining industry is summarized in this paper to provide the necessary perspective or tools to best assess the risks of rockburst occurrence in deep mines. The various classifications of rockbursts and their mechanisms are discussed. The paper also reviews the current empirical methods of rockburst prediction, which are mostly dependent on geomechanical parameters of the rock such as uniaxial compressive strength of the rock, as well as its tensile strength and elasticity modulus. At the end of this paper, some current achievements and limitations of empirical methods are discussed.

Technical challenges have always been part of underground mining activities, however, some of these challenges grow in complexity as mining occurs in deeper and deeper settings. One such challenge is rock mass stability and the risk of rockburst events. To overcome these challenges, and to limit the risks and impacts of events such as rockbursts, advanced solutions must be developed and best practices implemented. Rockbursts are common in underground mines and substantially threaten the safety of personnel and equipment, and can cause major disruptions in mine development and operations. Rockbursts consist of violent wall rock failures associated with high energy rock projections in response to the instantaneous stress release in rock mass under high strain conditions. Therefore, it is necessary to develop a good understanding of the conditions and mechanisms leading to a rockburst, and to improve risk assessment methods. The capacity to properly estimate the risks of rockburst occurrence is essential in underground operations. However, a limited number of studies have examined and compared yet different empirical methods of rockburst. The current understanding of this important hazard in the mining industry is summarized in this paper to provide the necessary perspective or tools to best assess the risks of rockburst occurrence in deep mines. The various classifications of rockbursts and their mechanisms are discussed. The paper also reviews the current empirical methods of rockburst prediction, which are mostly dependent on geomechanical parameters of the rock such as uniaxial compressive strength of the rock, as well as its tensile strength and elasticity modulus. At the end of this paper, some current achievements and limitations of empirical methods are discussed. [ABSTRACT FROM AUTHOR]