In-situ stress characteristics and rockburst prediction for a tunnel in a high in-situ stress environment
As the main source of pressure around tunnel rock masses, in-situ stress must be thoroughly understood to accurately predict the potential for rockburst events, especially in high-stress construction environments. Yet, theoretical advancements in this field remain behind practical engineering demands. In this study, we used a multiple linear regression approach to determine the initial in-situ stress field across the entire Ping’an tunnel and employed strength theory to predict rockburst occurrence. The regression results closely match field measurements, and the consistently low p-values confirm the reliability of the model. In addition, the rockburst intensity predicted based on the strength criterion shows strong agreement with real-world engineering observations, demonstrating the rationality of the proposed methodology. The findings from this research contributes to a deeper understanding of the initial in-situ stress field in rockburst-prone tunnels under high in-situ stress conditions and provide a scientific basis for tunnel alignment optimization and rockburst risk assessment. These insights hold significant practical implications for the construction of similar projects.
Authors
- Yonggang Zhang (ORCID: https://orcid.org/0000-0002-0028-2656)
- Maohong Yao (ORCID: https://orcid.org/0000-0002-5805-7654)
- Ying Yang (ORCID: https://orcid.org/0000-0002-9799-3293)
- Chun Luo
- Zhiwen Shen
- Meng Wang (ORCID: https://orcid.org/0000-0001-5090-6239)
- Jin Zhang (ORCID: https://orcid.org/0000-0001-5397-4376)
- Kai Zhang (ORCID: https://orcid.org/0000-0003-2656-523X)
- Shuang Yang (ORCID: https://orcid.org/0000-0002-1802-6335)
Institutions
- PowerChina (China) (CN)
- China Construction Eighth Engineering Division (China) (CN)
Publication Details
- Journal
- Frontiers in Earth Science
- Published
- 2025-08-25
- DOI
- https://doi.org/10.3389/feart.2025.1649835
- Citations
- 1
- Primary Topic
- Rock Mechanics and Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.86