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Record W2554699073 · doi:10.1139/cgj-2016-0260

Study on the efficiency of destress blasting in deep mine drift development

2016· article· en· W2554699073 on OpenAlexafffundvenueabout
Atsushi Sainoki, Muhammad Zaka Emad, Hani S. Mitri

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRock blastingRock mass classificationMining engineeringComputer simulationGeotechnical engineeringNumerical modelsGeologyEngineeringSimulation

Abstract

fetched live from OpenAlex

Canadian hard rock mines continue to reach deeper deposits, which poses greater challenges to mine safety including rock burst control. Destress blasting techniques have been successfully employed in such underground mines with the aim of preconditioning highly stressed rock mass to mitigate the risk for rock burst occurrence in deep mines. In the present study, the efficiency of destress blasting is examined through a comparison between traditional and alternative numerical modelling approaches. The traditional modelling approach assumes a uniformly distributed blast-induced damage zone extending over the entire drift face, whilst the alternative modelling approach, presented herein, simulates the damage zone for each individual blast hole. In the first part of this paper, a three-dimensional numerical model of a single blast hole is constructed, whereby the extent of blast-induced damage zone is delineated. The latter part of this paper uses the single-hole model results to examine the efficiency of destress blasting as practiced in drift development in deep mines. It is demonstrated through comparison of FLAC3D numerical simulation results that the traditional modelling approach may lead to an overly optimistic indication of destress blasting efficiency when compared with the alternative modelling approach, in which a more precise simulation of the damage zones is applied.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.212
Teacher spread0.192 · 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
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".

Quick stats

Citations43
Published2016
Admission routes4
Has abstractyes

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