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Record W2596898737

Large scale panel destress blasting parametric study

2016· article· en· W2596898737 on OpenAlexaff
Isaac Vennes, Hani S. Mitri

Bibliographic record

Venue3rd International Symposium on Mine Safety Science and Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsMcGill University
Fundersnot available
KeywordsPillarRock blastingStress reductionGeotechnical engineeringGeologyFenderPrincipal stressMining engineeringStress (linguistics)SpallParametric statisticsEngineeringStructural engineeringMathematicsPetrologyStatistics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this parametric study is to quantify the effect of panel destressing on a steeply dipping remnant ore pillar. A large-scale destress blast program is simulated in the hanging wall of the ore pillar using the finite difference program FLAC3D. The simplified model consists of a 10MT ore pillar divided into 20 stopes on two levels. Two panels are destressed in the hanging wall to cover 8 stopes, followed by the mining of 4 stopes in the stress shadow in a retreat sequence. The varied parameters are the rock fragmentation factor (α) and stress reduction factor (β) of the destress panel. The effect of panel destressing is evaluated based on the volume of ore at risk in the stress shadow as well as the sudden stress change in the stope caused by the destress blast. Overall, a successful blast with a realistic stress reduction factor and rock fragmentation factor reduces the major principal stress in the nearest stopes by 10MPa to 25MPa. This yields a reduction of ore at risk volume ranging from 8% to 50% in the stress shadow as the first 4 stopes are mined.

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 score0.388
Threshold uncertainty score0.531

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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