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Record W2887719732 · doi:10.1139/cgj-2018-0291

Influence of stress path on stress memory and stress fracturing in brittle rocks

2018· article· en· W2887719732 on OpenAlexafffundvenue
Navid Bahrani, Benoît Valley, Peter Kaiser

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsLaurentian UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStress pathBrittlenessGeotechnical engineeringGeologyStress (linguistics)Finite element methodExcavationComputer simulationOverburden pressureHydraulic fracturingStructural engineeringMaterials scienceEngineeringComposite materialPetrology

Abstract

fetched live from OpenAlex

A two-dimensional numerical model based on the distinct element method, previously calibrated to the laboratory properties of undamaged and damaged Lac du Bonnet (LdB) granite, was used to investigate the influence of stress path on the development of stress memory and stress fracturing in brittle rocks. Various cycles of loading and unloading, similar to those imposed during Kaiser effect tests, were first applied to undamaged numerical specimens of LdB granite. The results of Kaiser effect test simulations were found to be consistent with those of published laboratory and numerical investigations. Further simulations were conducted to investigate the influence of stress path resulting from the excavation of a tunnel on the depth of stress fracturing around the excavation boundary. For this purpose, the stress paths at points on and near the tunnel wall, obtained from a continuum finite element model, were applied to the calibrated numerical specimen. It was found that the amount of damage in the numerical specimens decreases rapidly with increasing distance from the excavation wall. The findings of this research shed some light on the influence of stress path and grain-scale heterogeneity on stress memory in brittle rocks and stress fracturing around underground openings.

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 score0.147
Threshold uncertainty score0.997

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 integrity0.0000.001
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations19
Published2018
Admission routes3
Has abstractyes

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