Understanding Mechanisms and Predicting Rockburst with Mathematical Methods
1Hunan University of Science and Technology, Xiangtan, China
2Wuhan University, Wuhan, China
3University of Malaya, Kuala Lumpur, Malaysia
4Central South University, Changsha, China
5Changsha University of Science & Technology, Changsha, China
Understanding Mechanisms and Predicting Rockburst with Mathematical Methods
Description
Rockburst is a common dynamic disaster in various fields of engineering, such as mining, tunnelling, and hydropower. This disaster usually involves the sudden ejection of rocks. The ejection of these rocks can be strong and harmful. With the development of the economy, geotechnical engineers are further exploring this scenario. The occurrence of rockburst is being reported in many countries. It has become a universal problem for engineers, imposing great challenges in engineering design, construction, and production.
Current investigations have shown that the occurrence of rockburst is highly correlated to the stress state, supporting conditions and excavation methods. Due to the complex characteristics of the rockburst phenomenon, many researchers worldwide have been interested in investigating the mechanical mechanisms and prevention measures for rockburst. In addition, with the advance of computer processing level, prediction methods using machine learning and artificial intelligence algorithms for evaluating rockburst have also been studied, especially for geotechnical projects. However, there is a need to further research in this field.
The aim of this Special Issue is to bring together original research and review articles covering the latest developments and challenges in understanding rockburst. We welcome articles that focus on mechanical mechanisms, prediction methods and prevention measures. Submissions discussing theoretical and numerical analyses from single or cross-discipline perspectives are welcome.
Potential topics include but are not limited to the following:
- Theoretical analysis of rockburst mechanism
- Numerical simulation of rockburst phenomenon
- Machine learning and artificial intelligence algorithms for evaluating rockburst potential
- Case studies related to rockburst
- Prevention and control using mathematical methods for the mitigation of rockburst in rock engineering