Research on rockburst volume classification and discriminant method based on microseismic information

被引：0
|
作者：
Liu G. [1 ]
Li S. [1 ]
Feng G. [2 ]
Chen B. [2 ]
Xu J. [1 ]
Du C. [1 ]
Chen X. [1 ]
机构：
[1] School of Highway, Changan University, Shaanxi, Xian
[2] State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Hubei, Wuhan
来源：
Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering | 2024年 / 43卷 / 03期
基金：
中国国家自然科学基金;
关键词：
clustering analysis; comprehensive discrimination; deep tunnel; microseismic information; rock mechanics; rockburst classification; rockburst volume;
D O I：
10.13722/j.cnki.jrme.2023.0598
中图分类号：
学科分类号：
摘要：
The hazard of a rockburst event is directly correlated with the scale of rock mass ejection in deep underground engineering. In order to further enhance the fine characterization and prediction level of rockburst hazards in deep underground engineering，a study was conducted on the classification and discrimination method of rockburst pit volume based on microseismic information. Firstly，a statistical analysis of one hundred and eleven rockburst cases from the deep tunnels of Jinping II hydropower station project was performed. It was found that six indicators，namely the cumulative number of microseismic events，cumulative microseismic energy release，cumulative microseismic volume，microseismic event rate，microseismic energy release rate and microseismic volume rate，showed a high correlation with the volume of rockburst pit. In other words，there was a significant hierarchical difference from low to high between the distribution of rockburst volumes and the values of microseismic parameters. Secondly，a hierarchical clustering analysis was employed to establish a classification scheme for rockburst volumes，taking into consideration engineering practicality and predictability. Taking the Jinping tunnel project as an example，the rockburst volume was divided into five levels，and the corresponding volume thresholds for each level were then determined. Finally，a decision tree based on the improved classification and regression tree(CART) algorithm was constructed to determine discrimination thresholds for various microseismic parameters under different rockburst volume levels，and six individual microseismic parameter criteria for rockburst volume classification were therefore developed. Furthermore，a spider web diagram method based on multiple microseismic parameters was developed for comprehensive discrimination of rockburst classification，and corresponding discrimination criteria were determined through case analysis. This method enables people to rapidly discriminate the potential level of rockburst volume during tunnel excavation. The results from retrospective verification of the collected rockburst cases showed an overall accuracy rate of 85.2% for rockburst volume discrimination，demonstrating a high accuracy and applicability of the proposed method. This research provides a new and effective approach to improve the fine prediction level of rockburst hazards in similar deep underground engineering projects. © 2024 Academia Sinica. All rights reserved.
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页码：683 / 697
页数：14
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