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Record W1998133801 · doi:10.1002/nag.222

Probabilistic analysis of underground pillar stability

2002· article· en· W1998133801 on OpenAlexaff
D. V. Griffiths, Gordon A. Fenton, Carisa B. Lemons

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2002
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsDalhousie University
FundersNational Science Foundation
KeywordsPillarStability (learning theory)Probabilistic logicGeotechnical engineeringSafety factorMonte Carlo methodReliability (semiconductor)Context (archaeology)Finite element methodCompressive strengthRandom fieldFactor of safetyStructural engineeringEngineeringSlope stabilityMathematicsComputer scienceGeologyStatisticsMaterials science

Abstract

fetched live from OpenAlex

Abstract The majority of geotechnical analyses are deterministic, in that the inherent variability of the materials is not modelled directly, rather some ‘factor of safety’ is applied to results computed using ‘average’ properties. In the present study, the influence of spatially varying strength is assessed via numerical experiments involving the compressive strength and stability of pillars typically used in underground construction and mining operations. The model combines random field theory with an elasto‐plastic finite element algorithm in a Monte‐Carlo framework. It is found that the average strength of the rock is not a good indicator of the overall strength of the pillar. The results of this study enable traditional approaches involving factors of safety to be re‐interpreted as a ‘probability of failure’ in the context of reliability based design. Copyright © 2002 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.340
Teacher spread0.295 · 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

Citations52
Published2002
Admission routes1
Has abstractyes

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