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Record W4386523081 · doi:10.56952/arma-2023-0127

Investigation of Excavation Length Effect on Stope Stability at a Canadian Hard Rock Mine

2023· article· en· W4386523081 on OpenAlexaffabout
Huawei Xu, B. Apel Derek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsExcavationRock mass classificationGeotechnical engineeringMining engineeringDisplacement (psychology)Underground mining (soft rock)StopingGeologyEngineeringCoal mining

Abstract

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ABSTRACT In underground mining, stopes are excavated step by step, and each step has a certain length according to the mine planning. Displacements of the free surface of the stope sidewall are contributed by the excavation of stopes. Rockburst is caused by the exaction in underground stopes in rockburst proneness rock mass, especially in deep underground mines. In this paper, the stope excavation length effects on the displacement and rockburst in underground stopes induced by excavation were investigated by numerical modelling analysis. Five different stope excavation length scenarios were proposed and performed to study the effect of excavation length on the stope stability at a Canadian hard rock mine. With different increasing phases during displacement initiation and rockburst development, all five excavation scenarios achieved almost the same final results in rockburst tendency and displacement at the analyzed location on the stope sidewalls. Compared with the other four excavation scenarios, scenario SCN#1 is more effective and efficient in numerical simulation analysis, especially for the numerical simulation analysis of the full-size underground mine. INTRODUCTION As underground mining works progress into deeper and more complex geological environments, they are experiencing more stress-induced rock damage initiation problems, which have seriously impeded mining efficiency and effectiveness (Kaiser et al., 2000). To better understand underground mining stope convergence and deal with the rock mass damages during the excavation, many researchers are actively addressing these issues. Barla (Barla et al., 2010; Barla et al., 2012; Barla & Pelizza, 2000) proposed several approaches for stope design by assessing the interaction between rock mass and structures in rock mass with time-dependent squeezing behavior. Janoszek (Janoszek, 2020) proposed two indexes to predict natural hazards in longwall working with analysis of coal and roof properties, interaction in shield loading and roof-floor by numerical modelling based on the Mohr-Coulomb criterion. Rock mass squeezing phenomenon around the stopes are widely investigated by considering the different rates of advancing (Ghaboussi & Gioda, 1977), analyzing the extension of microcrack length and development of excavation damage zone (Golshani et al., 2007; Tang et al., 2018), time-dependent deformation, and elastoplastic behavior (Malan, 2002), in the means of semi-empirical back analysis approach (Manh et al., 2015), analytical solution (Sulem et al., 1987), and numerical modelling (Ghaboussi & Gioda, 1977; Golshani et al., 2007; Manh et al., 2015; Wang & Huang, 2011; Weng et al., 2010; Xu & Apel, 2020). Gioda (Gioda & Cividini, 1996) discussed the linear and non-linear viscous constitutive laws and developed numerical methods to analyze the time-dependent effect on performance in squeezing rocks. The interaction mechanism between the stope excavation and stope stability was studied by analyzing the supports, physical model tests, underground research laboratory measurement, and numerical simulation (e Sousa et al., 2012; Funatsu et al., 2008; Martino & Chandler, 2004; Vazquez-Silva et al., 2020; Xu, 2021; Yuan & Yang, 2021; Zhang et al., 2019).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.526

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.207
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes2
Has abstractyes

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