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Record W4280587193 · doi:10.1680/jgeot.21.00293

Instability of gold mine tailings subjected to undrained and drained unloading stress paths

2022· article· en· W4280587193 on OpenAlexaff
Amirreza Fotovvat, Abouzar Sadrekarimi, Michael Etezad

Bibliographic record

VenueGéotechnique · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsGolder Associates (Canada)Western University
Fundersnot available
KeywordsInstabilityTailingsGeotechnical engineeringStress (linguistics)Stress pathShear stressGeologyShear (geology)Effective stressVoid ratioMaterials scienceCylinder stressComposite materialMetallurgyMechanics

Abstract

fetched live from OpenAlex

The drained instability of two gold mine tailings under lateral stress relief is investigated in this study. Constant deviator stress (CDS) unloading tests were performed using a triaxial apparatus to examine instability imposed by unloading in a drained condition. Instability was induced by enforcing a constant deviator stress while simultaneously reducing the mean effective stress. Stress paths and shear strengths of CDS tests were compared with undrained triaxial tests on comparable specimens which were anisotropically consolidated to the same initial stress ratios of the CDS tests. Several techniques were employed to determine the onset of instability in the CDS tests. The effects of unloading rate and initial stress ratio on the behaviour and the triggering of instability in the CDS tests were further investigated. It was observed that specimens consolidated to the same void ratio and initial stress ratio undergo instability at similar stress ratios or friction angles in both CDS and undrained shear tests. This suggests that the instability characteristic of tailings prone to stress relief can be predicted using undrained triaxial tests on anisotropically consolidated specimens. Critical states and state parameters of specimens subjected to CDS and unloading stress paths were also compared and analysed.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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