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Record W4318070666 · doi:10.1680/jgeot.21.00210

The full state of stress in monotonic simple shear tests on sand

2023· article· en· W4318070666 on OpenAlexaff
Mason Ghafghazi, Mark Talesnick, Farid Ahmadi Givi

Bibliographic record

VenueGéotechnique · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeotechnical engineeringSimple shearConsolidation (business)Shear (geology)Monotonic functionPore water pressureEffective stressGeologyStress pathTriaxial shear testShear stressDirect shear testPrincipal stressMaterials scienceMathematicsComposite material

Abstract

fetched live from OpenAlex

The simple shear test has been an important tool in practical geotechnical engineering and the study of soil behaviour. The attractiveness of the simple shear test is founded in its relative ease of specimen preparation, similarity to at-rest conditions during consolidation and rotation of principal stresses during shear. Despite this test's attractive qualities, there are significant deficiencies in the interpretation of standard test results: horizontal normal stresses are not known and excess pore pressures are often interpreted from assumptions about constant-volume tests without saturation. To address this problem, a set of monotonic tests were performed on a sand, during which horizontal normal stresses were measured in the central portion of specimens. Based on the measurements, the full state of stress was defined, stress paths were drawn and directions of the principal stresses were determined. Outcomes showed that the conventional approach of estimating excess pore pressures as the change in vertical pressure to maintain constant volume in response to shear is problematic, and friction angles interpreted from conventionally measured shear and vertical stresses may be grossly underestimated in some cases.

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 score0.292
Threshold uncertainty score0.415

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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