Prediction and classification technology of rockburst hazard in deep buried and high in-situ stress tunnel

With the increasing number of deep-buried tunnel constructions in China, the possibility of rockburst hazard during tunnel construction in high-stress, hard rock environments also rises. The stress inversion method is initially employed to determine the stress characteristics along the tunnel axis to provide the basis for predicting rockburst hazards. Then, utilizing widely recognized stress criteria and newly proposed energy criteria, the risk of tunnel rockburst hazard is predicted from macroscopic and microscopic perspectives. The results indicated that: (1) The discrepancy between stress inversion and borehole measurement data is generally within 13%, confirming the feasibility of obtaining the in-situ stress distribution characteristics of the tunnel site through stress inversion. (2) From the macro perspective, the predictive accuracies for the left and right tubes in assessing the level of rockburst hazard in the Ping'an Tunnel are 70.6% and 68.7%, respectively. The accuracy of the rockburst hazard level prediction presented in this study is approximately 70%, achieving a high reliability level. (3) From the microscopic perspective, based on the energy criterion of rockburst hazard and the energy evolution of the surrounding rock during the construction process of the Ping'an Tunnel, the specific locations where rockburst hazard can occur on the tunnel face are analyzed. This provides a vital reference for the prevention and control of rockburst hazard.

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Publication Details

Journal
Scientific Reports
Published
2025-03-20
DOI
https://doi.org/10.1038/s41598-025-93351-4
Citations
3
Primary Topic
Rock Mechanics and Modeling
Type
article
Field-Weighted Citation Impact
2.59

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article

Prediction and classification technology of rockburst hazard in deep buried and high in-situ stress tunnel

Yuxin Yang, Zhiqiang Zhang, Hui Li, Li Tang
3 citations
Scientific Reports
Rock Mechanics and Modeling
2.59
article

Prediction and classification technology of rockburst hazard in deep buried and high in-situ stress tunnel

Yuxin Yang, Zhiqiang Zhang, Hui Li, Li Tang
article en
3 citations

Abstract

With the increasing number of deep-buried tunnel constructions in China, the possibility of rockburst hazard during tunnel construction in high-stress, hard rock environments also rises. The stress inversion method is initially employed to determine the stress characteristics along the tunnel axis to provide the basis for predicting rockburst hazards. Then, utilizing widely recognized stress criteria and newly proposed energy criteria, the risk of tunnel rockburst hazard is predicted from macroscopic and microscopic perspectives. The results indicated that: (1) The discrepancy between stress inversion and borehole measurement data is generally within 13%, confirming the feasibility of obtaining the in-situ stress distribution characteristics of the tunnel site through stress inversion. (2) From the macro perspective, the predictive accuracies for the left and right tubes in assessing the level of rockburst hazard in the Ping'an Tunnel are 70.6% and 68.7%, respectively. The accuracy of the rockburst hazard level prediction presented in this study is approximately 70%, achieving a high reliability level. (3) From the microscopic perspective, based on the energy criterion of rockburst hazard and the energy evolution of the surrounding rock during the construction process of the Ping'an Tunnel, the specific locations where rockburst hazard can occur on the tunnel face are analyzed. This provides a vital reference for the prevention and control of rockburst hazard.

Scientific ReportsVol. 15(1)
Southwest Jiaotong University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 12%
Rock Mechanics and Modeling
2.59
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Prediction and classification technology of rockburst hazard in deep buried and high in-situ stress tunnel — Yuxin Yang, Zhiqiang Zhang, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS