Intelligent Disaster Early Warning and Risk Control in Underground Mines

Underground mineral extraction is extending to greater depths where geological conditions and operational environments are progressively more complex. Interactions among high geostress, large-scale mined-out areas, hazardous gases, dust, equipment disturbances and human activities may generate dynamically coupled and rapidly evolving safety risks. Conventional safety management methods, which mainly rely on single-parameter monitoring and post-accident responses, are insufficient for accurately identifying the complex relationships among hazard sources, operational environments, worker conditions and safety management.

Recent advances in intelligent sensing, artificial intelligence, multi-source information fusion, wearable monitoring, knowledge graphs and digital platforms have provided new opportunities for improving the accuracy, timeliness and reliability of underground mine disaster prediction and risk control. 

This Special Issue aims to provide a global platform for presenting theoretical advances, technological developments and engineering applications in intelligent disaster early warning, prevention and risk control for underground mines. It will promote the practical application of intelligent safety technologies and strengthen the integration of academic research with industrial practice. The Special Issue welcomes original research articles, review papers, and selected case studies on topics including, but not limited to, the following:

• Multi-scale rock mass damage and fracture mechanism in underground mining
• Underground goaf instability characteristics and dynamic disaster evolution
• Multi-source intelligent monitoring & early warning for complex underground mines
• Prevention and control of gas, dust, explosion and underground environmental hazards
• High-precision personnel positioning in complex underground mining spaces
• Miner multimodal state recognition and individual risk assessment
• Coupling mechanisms and dynamic evaluation of human-equipment-environment-management risks
• AI, edge computing, digital twins for data-driven mine safety management
• Integrated platforms for disaster warning, risk control and emergency decision-making
• Mine safety risk assessment, accident prevention and emergency management

To submit your paper, please follow the submission guidelines and indicate that your submission is intended for the special issue on “Mine Disaster Warning and Risk Control”. We look forward to receiving your contributions and fostering the dialogue on this critical topic.

Once your manuscript is accepted, it will proceed to production and be published simultaneously both in the current regular issue and as part of the online Special Issue. While the articles may appear across different regular issues, they will be clearly marked and grouped under the Special Issue theme.

Submission guidelines

Please read the [Guide for Authors] before submitting. All articles should be [submitted online], please select [VSI: Mine Disaster Warning and Risk Control] on submission.

Important Dates

• Submission begins: 20 September 2026
• Submission deadline: 30 September 2027

Guest Editors

• Prof. Gang Wang
  Shandong University of Science and Technology, China.
  Email: gang.wang@sdust.edu.cn
• Prof. Khavalbolot Kyelgyenbai
  Mongolian University of Science and Technology, Mongolia.
  Email: khavalbolot@must.edu.mn
• Assoc. Prof. Atsushi Sainoki
  Kumamoto University, Japan.
  Email: atsushi_sainoki@kumamoto-u.ac.jp