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Record W4401479294 · doi:10.56952/arma-2024-0152

Probabilistic Stope Stability Analysis Based on the Modified Stability Graph

2024· article· en· W4401479294 on OpenAlexaboutno aff
Cyrille Séguineau de Préval, A. Ouellet, Patrick Andrieux

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
Fundersnot available
KeywordsProbabilistic logicStability (learning theory)Computer scienceGraphTheoretical computer scienceArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

ABSTRACT: The Stability Graph method is widely used to assess the stability of a stope. However, its traditional deterministic application overlooks the variability of the underlying rock mass parameters. By defining distribution functions for the rock mass parameters and running a Monte Carlo analysis on these inputs, the inherent rock mass variability is reflected in the outcome in the form of a distribution of the modified stability number N'. This paper presents guidelines on defining the input distribution functions, and particularly on elaborating the correlation between these distributions. Finally, methods are presented to interpret the obtained modified stability number distribution. The first method introduces a risk tolerance factor allowing to completely determine a stable stope. The second method identifies the drivers for instability, which could be used as variables for subsequent stope optimization. 1. STABILITY GRAPH METHOD The sizing of open stopes for underground mines is of particular importance during the mine design. Several tools are available to support the engineers throughout the process. One of the most widely used methodology in Canada is the empirical Stability Graph Method, initially developed by Mathews et al. (1981). This method was modified by Potvin (1988) to better account for the specificities of Canadian hard rock mines. 1.1. Modified Stability Graph Method The Stability Graph Method involves the calculation of two parameters: the modified stability number N′ and the hydraulic radius HR. The modified stability number is defined as follows: (equation) where Q′ is the Q-system rock mass quality as defined by Barton (1974) with the stress reduction and water factors set to 1.0, and A, B and C the rock stress, joint orientation adjustment and surface orientation factors, respectively. The modified Q′ value is defined by the following equation: (equation) with RQD the rock quality designation index, Jn the joint set number, Jr the joint roughness number and Ja the joint alteration number (Barton, 1974). The three parameters A, B and C are evaluated graphically as shown in figures 1, 2 and 3, respectively.

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 categoriesInsufficient payload (model declined to judge)
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 score0.242
Threshold uncertainty score0.997

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)0.0040.000

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 head0.032
GPT teacher head0.222
Teacher spread0.189 · 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.

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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