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Modelling of progressive failure mechanism of mine pillars

2023· article· en· W4315482689 on OpenAlexaff
G. Cammarata, Davide Elmo, Sandro Brasile

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2023
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDilatantConstitutive equationBrittlenessViscoplasticitySofteningGeologyNonlinear systemGeotechnical engineeringShearing (physics)MechanicsShear (geology)Materials scienceStructural engineeringEngineeringPhysicsFinite element methodComposite material

Abstract

fetched live from OpenAlex

Abstract Rock fracturing process around underground openings is mainly a process of progressive slabbing with the generation of surface-parallel fractures in the initial stage, and shear failure is likely to occur in the final process. The difficulty of capturing this behaviour through conventional continuum modelling has led to the development of advanced constitutive laws for use in continuum models. Recently, an enhanced continuum constitutive approach to simulate strain-softening based on the Hoek-Brown failure criterion has been presented. This advanced Hoek-Brown model with Softening introduces a hyperbolic decay of the material properties affecting the post-peak response and the nonlinear dilation, thus enabling to investigate failure modes in the form of dilatant shear bands. Moreover, to restore the objectivity of the numerical solution during the development of strain localization phenomena, a viscous regularization technique has been implemented within the model. The performance of this constitutive model has been proved and, in this paper, further numerical computations are reported concerning the brittle failure process occurring in mine pillars, thus confirming the capability to capture failure mechanisms during excavation within a strain localization regime.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.370

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 head0.017
GPT teacher head0.188
Teacher spread0.172 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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