基于大样本不完整数据的岩爆致因特征及预测模型

刘国锋; 杜程浩; 丰光亮; 晏长根; 李胜峰; 徐鼎平

doi:10.3799/dqkx.2022.491

Volume 48 Issue 5

May 2023

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Article Navigation > Earth Science > 2023 > 48(5): 1755-1768

Liu Guofeng, Du Chenghao, Feng Guangliang, Yan Changgen, Li Shengfeng, Xu Dingping, 2023. Causative Characteristics and Prediction Model of Rockburst Based on Large and Incomplete Data Set. Earth Science, 48(5): 1755-1768. doi: 10.3799/dqkx.2022.491

Citation:

Liu Guofeng, Du Chenghao, Feng Guangliang, Yan Changgen, Li Shengfeng, Xu Dingping, 2023. Causative Characteristics and Prediction Model of Rockburst Based on Large and Incomplete Data Set. Earth Science, 48(5): 1755-1768. doi: 10.3799/dqkx.2022.491

Citation:

Liu Guofeng, Du Chenghao, Feng Guangliang, Yan Changgen, Li Shengfeng, Xu Dingping, 2023. Causative Characteristics and Prediction Model of Rockburst Based on Large and Incomplete Data Set. Earth Science, 48(5): 1755-1768. doi: 10.3799/dqkx.2022.491

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Causative Characteristics and Prediction Model of Rockburst Based on Large and Incomplete Data Set

doi: 10.3799/dqkx.2022.491

Liu Guofeng^{1, 2
,
,},
Du Chenghao^{1, 2},
Feng Guangliang^{3
,
,},
Yan Changgen^{1, 2},
Li Shengfeng^{1, 2},
Xu Dingping³

1.
School of Highway, Chang'an University, Xi'an 710064, China
2.
Key Laboratory for Bridge and Tunnel of Shaanxi Province, Chang'an University, Xi'an 710064, China
3.
State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China

Received Date: 2022-10-08
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

In order to distinguish the sensitivity factors affecting rockburst and construct a rockburst prediction method under the condition of incomplete data cases, a large sample database is established on the basis of collecting 429 groups of rockburst cases at home and abroad, and the distribution characteristics and regulation of rockburst disaster-inducing factors were summarized. Six evaluation indexes, including buried depth, uniaxial compressive strength of rock, uniaxial tensile strength of rock, maximum tangential stress of surrounding rock, rock elastic energy index and integrity coefficient of rock mass, are selected to establish a rockburst probability prediction model based on large and incomplete data set by using Bayesian network, and the sensitivity analysis and engineering application are carried out. Through analysis, it is found that the maximum tangential stress of surrounding rock and the integrity coefficient of rock mass have a great influence on rockburst. The model has a prediction coincidence rate of 83.3% for rockburst cases with information loss rate of 20%, and the prediction effect is better than the commonly used empirical criterion of rockburst. The results show that the prediction indexes selected in this paper can comprehensively consider the influencing factors of rockburst, and the established model has applicability and reliability for the prediction of deep rockburst disasters.
- rockburst,
- disaster-inducing factor,
- sensitivity analysis,
- probabilistic prediction,
- incomplete data set,
- hazard geology

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References(63)

References

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