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Record W4297231899 · doi:10.56952/arma-2022-0042

Stability Analysis of Shotcrete Lining for a Mine Shaft Using the Finite-Discrete Element Method

2022· article· en· W4297231899 on OpenAlexaff
Saeed Naseri, Navid Bahrani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsDalhousie University
Fundersnot available
KeywordsShotcreteExtensometerRock mass classificationGeotechnical engineeringFinite element methodExcavationGeologyDrillRock boltEngineeringStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

ABSTRACT: This paper aims to provide insights into the effectiveness and failure mechanism of shotcrete lining in mine developments. For this purpose, a two-dimensional numerical program based on the Finite-Discrete Element Method (FDEM) was utilized to simulate an instrumented section of a 10-m diameter mine shaft at a depth of 1.2 km in an average quality rock mass. The shaft wall was supported by shotcrete and concrete liners installed at 3 m and 12 m behind the face, respectively. However, only shotcrete support was considered in the numerical simulations. The shotcrete liner was modelled as a material with a thickness of 50 mm and calibrated based on the mechanical properties of early-age fiber reinforced shotcrete obtained from available laboratory test results and empirical equations. The rock material near the modelled shaft was calibrated against the extensometer measurement data. It was found that the shotcrete liner fails due to bending caused by the shaft wall convergence and local rock mass bulking. Therefore, for the rock mass and in situ stress conditions representative of the mine shaft, it is concluded that the addition of a permanent support element, such as a concrete liner, is necessary for the long-term stability of the shaft. 1. INTRODUCTION Underground mines aim to reach deep orebodies by enhancing the advance rate of access developments while maintaining safe workplaces. Underground lateral (drifts) and vertical (shafts) developments are excavated using two main methods: drill and blast and mechanized excavations. The application of mechanized excavation machinery leads to an increase in the overall performance and thus the net present value of the projects. While mechanized excavation is ideal for moderately strong to weak rock masses, the drill and blast method has been used in a wide range of rock mass conditions [1]. Although the mining industry has been investigating mechanized excavation as an alternative method in hard rocks, the conventional drill and blast excavation method is still the norm in modern-day developments in underground mines [2].

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.287
Teacher spread0.252 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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