AUTHOR=Li Zi-Bin , Qiao Deng-Pan , Yang Tian-Yu 

TITLE=Stability control of open stopes in high-stress deep mining: a structural parameter design methodology based on the improved mathews stability graph method

JOURNAL=Frontiers in Earth Science

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1610234

DOI=10.3389/feart.2025.1610234

ISSN=2296-6463

ABSTRACT=IntroductionWith 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”).MethodsTaking 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.ResultsFirst, 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.DiscussionFinally, 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.