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Record W2616369261 · doi:10.1520/gtj20160159

Behavior of a Sensitive Clay in Isotropically Consolidated <i>Ko</i>-Drained Triaxial Tests

2017· article· en· W2616369261 on OpenAlexaffabout
Vincenzo Silvestri, Ghassan Abou‐Samra

Bibliographic record

VenueGeotechnical Testing Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsGeotechnical engineeringTriaxial shear testGeologyOverburden pressureShear (geology)Shear stressDeformation (meteorology)Stress (linguistics)Effective stressFriction angleMaterials scienceComposite materialPetrology

Abstract

fetched live from OpenAlex

Abstract This paper presented experimental results obtained in isotropically consolidated K̇o-drained triaxial tests on a lightly overconsolidated sensitive clay from Louiseville (Quebec). For the tests that start near the origin, the results showed that the response of the structured clay can be divided into three distinct phases of deformation. At low mean effective stress levels, the value of K̇o ranged between 0.2 and 0.3, and the shear stress varied linearly with strain. A critical shear stress was reached at the end of the first phase of deformation. At intermediate stress levels, the clay behaved like a plastic material, with K̇o = 0 and the shear stress remained approximately constant during deformation. At high stress levels, the clay becomes normally consolidated with K̇o = 1 − sin ϕ′, where ϕ′ is the friction angle of the destructured clay. However, the clay does not become normally consolidated for vertical effective stresses just in excess of the vertical preconsolidation pressure σ′vp. Rather, the effective vertical stress must exceed 1.36 σ′vp for the clay to reach a normally consolidated state. Comparisons were made with test results obtained on other sensitive clays of Eastern Canada, which showed a response similar to that of Louiseville clay.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.028
GPT teacher head0.260
Teacher spread0.232 · 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.

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

Citations3
Published2017
Admission routes2
Has abstractyes

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