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Record W4388410296 · doi:10.2991/978-94-6463-258-3_14

Continuum-Based Voronoi Tessellated Models for Capturing Unloading-Induced Brittle Damage in Hard Rocks

2023· book-chapter· en· W4388410296 on OpenAlexafffundabout
Fatemeh Amiri, Navid Bahrani

Bibliographic record

VenueAtlantis highlights in engineering/Atlantis Highlights in Engineering · 2023
Typebook-chapter
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsVoronoi diagramBrittlenessGeologyMaterials scienceGeometryComposite materialMathematics

Abstract

fetched live from OpenAlex

The continuum numerical program RS2 was used to generate a Voronoi Tessellated Model (VTM), consisting of blocks meshed into several triangular elements and block boundaries simulated using joint elements.The RS2-VTM was calibrated to the compressive and tensile strengths of undamaged Lac du Bonnet (LdB) granite.The simulation results indicated a reasonable agreement between the peak strength and post-peak response of the RS2-VTM and those of LdB granite.Next, simplified 3D coring stress paths for horizontal and vertical boreholes at the 420 level of Canada's Underground Research Laboratory (URL) were applied to the calibrated RS2-VTM.The simulated core damage (i.e., yielded joint elements in RS2) was found to be comparable to that of discontinuum models.Finally, 2D and 3D stress paths experienced by the rock mass in the roof of the Mine-by Experiment (MBE) tunnel at the URL were applied to the calibrated RS2-VTM.It was found that the 3D stress path causes more damage compared to the 2D stress path due to the tensile stress generated ahead of the MBE tunnel face, which is not captured in 2D models.

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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.203
Teacher spread0.184 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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