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Record W2613383486

Numerical modelling of time-dependent skin degradation of an isolated pillar

2016· article· en· W2613383486 on OpenAlexaff
Atsushi Sainoki, 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
KeywordsDegradation (telecommunications)PillarInstabilityBrittlenessWeibull distributionSofteningCreepBreakageParametric statisticsGeotechnical engineeringMechanicsMaterials scienceGeologyStructural engineeringComposite materialMathematicsEngineeringPhysicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

The present study focuses on the mechanism of instability of an isolated pillar caused by time-dependent skin degradation and strength heterogeneity. The time-dependent skin degradation is simulated with a non-linear rheological model capable of simulating tertiary creep. The inherent strength heterogeneity is realized with the Weibull’s distribution. Results obtained from the analysis shows that the skin degradation is limited to regions near the surface until two months after extracting ore, but afterwards it starts to extend deeper into the inside, eventually leaving a highly stressed pillar core due to the stress transfer from the failed rock. Rockburst potential indices show that the risk increases exponentially at the core as time goes by. It is then demonstrated that the progressive skin degradation cannot be simulated with the conventional strain-softening model assuming brittle failure. The parametric study with respect to the degree of heterogeneity reveals that the heterogeneity is a key to the occurrence of progressive skin degradation. Although average UCS in the model with high degree of heterogeneity is almost the same as that in the model with low degree of heterogeneity, the degradation of rockmass extends deep into the pillar only in the highly heterogeneous model.

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 score0.484
Threshold uncertainty score0.368

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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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