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Record W4402545400 · doi:10.36487/acg_repo/2465_93

Three-dimensional modelling of de-stressed rock mass using classification systems

2024· article· en· W4402545400 on OpenAlexaboutno aff
Shahé Shnorhokian, Samar Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRock mass classificationComputer scienceGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

De-stressing and preconditioning blasts have been used in the past 70 years at underground mines on all continents. The main design approach for implementing the technique has been empirical in nature and an engineering assessment methodology was developed only at the turn of the century. As mining progresses to increasing depths, additional tools are required to evaluate the impact of various de-stressing techniques and designs. Numerical modelling has been adopted since the early 1970s to simulate de-stressed rock masses and to aid in the assessment of the resulting stress distributions. In most cases, a percentage reduction (often by an arbitrary quantity) in the deformation modulus Erm has been used to represent the de-stressed regions. Rock fragmentation and stress dissipation factors have constituted another approach used in modelling the affected areas. In this paper, the rock mass rating (RMR) classification scheme is used as the basis for determining model input properties for de-stressed regions. Based on an extensive literature review, it is shown that the two main mechanisms responsible for de-stressing are the creation of new fractures or slippage of blocks on preexisting ones. The RMR allocates 20 points each to the rock quality designation (RQD) and spacing of discontinuities, which can be incrementally reduced to account for the first mechanism. The conditions of discontinuities constitute another 30 points that can be used to adequately represent the second mechanism. Using a simplified 3D model of a typical mine in the Canadian Shield, the Erm is calculated at an underground drift and crosscut system based on relative reductions in the RMR corresponding to both destressing mechanisms. Not only is the new approach found to be suitable for design purposes by validating it against reported field measurements, but the changes in the rock mass classification represent physical modifications that can be observed and that make sense from a rock mechanics perspective.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.098
GPT teacher head0.242
Teacher spread0.144 · 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 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
Published2024
Admission routes1
Has abstractyes

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