ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 13 - 2025 | doi: 10.3389/feart.2025.1610234
This article is part of the Research TopicGeological Hazards in Deep Underground Engineering: Mechanism, Monitoring, Warning, and ControlView all 4 articles
Stability Control of Open Stopes in High-Stress Deep Mining: A Structural Parameter Design Methodology Based on the Improved Mathews Stability Graph Method
Provisionally accepted- Kunming University of Science and Technology, Kunming, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
With the depletion of shallow mineral resources and surging demand for deep mining, metal mines in China have generally entered the kilometer-depth mining stage, confronting challenges in stope stability caused by high ground stress, elevated rock temperatures, high seepage pressure, and intense mining disturbances ("three highs and one disturbance"). Taking the Dahongshan Copper Mine (800-1000 m depth) as a case study, this paper proposes an enhanced design methodology for structural parameters of deep open stopes to address the limitations of the traditional Mathews stability graph method in 3D mechanical characterization, dynamic evolution analysis, and model generalization. First, an improved stability graph model was developed by refining hydraulic radius calculations through cross-sectional collaborative analysis and establishing quantifiable zoning thresholds for span and exposure area based on geological variations between eastern and western ore sections. Second, time-series cavity scanning revealed dynamic evolution patterns of stope stability, demonstrating that hydraulic radius and collapse height peak post-blasting. This finding highlights the pre-final blasting state as the critical node for stability evaluation. An ensemble model integrating Stacking, Bagging, Boosting, and Voting strategies demonstrated significant improvements in prediction accuracy and classification performance over traditional logistic regression. Finally, validation in high-stress stopes at 600-1000 m depths confirmed the model's generalization capability, offering a data-mechanism dual-driven decision framework for structural parameter design in deep open stopes.
Keywords: Mathews stability graph, open stoping with subsequent backfill, Structural parameters, ensemble learning, Deep mining
Received: 11 Apr 2025; Accepted: 29 May 2025.
Copyright: © 2025 Li, Qiao and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dengpan Qiao, Kunming University of Science and Technology, Kunming, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.