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Record W3130070633 · doi:10.1520/jte20200298

Effect of Unloading Direction on Rock Failure under True Triaxial Stress Conditions

2021· article· en· W3130070633 on OpenAlexaff
Peng Jia, Nan Yang, Dongqiao Liu

Bibliographic record

VenueJournal of Testing and Evaluation · 2021
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsGeomechanica (Canada)
FundersCentral Universities in ChinaState Key Laboratory for GeoMechanics and Deep Underground EngineeringNatural Science Foundation of Liaoning Province
KeywordsGeotechnical engineeringPrincipal stressFailure mode and effects analysisInstabilityGeologyShear (geology)Stress (linguistics)Ultimate tensile strengthHoek–Brown failure criterionFailure mechanismMaterials scienceStructural engineeringRock mass classificationComposite materialEngineeringMechanicsPetrology

Abstract

fetched live from OpenAlex

Abstract Rock failures induced by excavation unloading in the direction of the minimum principal stress σ3 and the intermediate principal stress σ2 were numerically simulated and analyzed. The damage evolution process, the failure mode, and the failure mechanism under the two conditions were investigated. The results show that the unloading direction of the principal stress has a significant impact on the rock failure mode. Under the same triaxial stress level, when unloading σ3, rock failure is mainly caused by localized tensile-shear composite failure; when unloading σ2, rock failure is mainly caused by tensile buckling failure. When unloading σ3, the damage is mainly concentrated near the free surface; however, when unloading σ2, the damage range increases and develops toward the interior of the rock. The greater the intermediate principal stress is before unloading, the larger the number of splitting fractures and the magnitude of dilatation are, and the smaller the thickness of the splitting plate is. These results may shed light on the prevention and mitigation of rock instability in deep underground engineering.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.035
GPT teacher head0.304
Teacher spread0.269 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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