Paper
10 November 2021 Current status and prospects of research on rockburst prediction in deep tunnels
Xinrui Zhao
Author Affiliations +
Proceedings Volume 12050, International Conference on Smart Transportation and City Engineering 2021; 120505Q (2021) https://doi.org/10.1117/12.2614410
Event: 2021 International Conference on Smart Transportation and City Engineering, 2021, Chongqing, China
Abstract
Rockburst prediction is very important to alleviate rockburst hazards. Due to the numerous influencing factors of rockburst and the complicated occurrence mechanism, the accuracy and applicability of the three widely used rockburst prediction methods (based on theoretical criteria, case analysis, and on-site monitoring) still need to be strengthened. This paper systematically summarized the research status of the three types of rockburst prediction methods, analyzed the advantages and disadvantages of various rockburst prediction methods, and looked forward to the future research direction of rockburst prediction. First of all, More theoretical criteria for rockburst prediction should be put forward. Second, large data sets of rockburst should be further collected and organized to improve the accuracy and applicability of rockburst prediction methods based on case analysis. Third, the precursor information contained in the signal obtained by the on-site monitoring method should be further excavated.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xinrui Zhao "Current status and prospects of research on rockburst prediction in deep tunnels", Proc. SPIE 12050, International Conference on Smart Transportation and City Engineering 2021, 120505Q (10 November 2021); https://doi.org/10.1117/12.2614410
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Analytical research

Machine learning

Acoustic emission

Evolutionary algorithms

Mining

Resistance

Detection and tracking algorithms

Back to Top