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Record W2414157281 · doi:10.1139/cgj-2015-0598

Semi-empirical elastic–thermoviscoplastic model for clay

2016· article· en· W2414157281 on OpenAlexaffvenue
David Kurz, Jitendra Sharma, Marolo Alfaro, Jim Graham

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsYork UniversityUniversity of ManitobaKGS Group (Canada)
FundersMinistère de l'Enseignement supérieur, de la Recherche et de l'Innovation
KeywordsViscoplasticityCreepGeotechnical engineeringPlasticityShear (geology)Materials scienceCompression (physics)Strain rateStress (linguistics)Effective stressShear stressConstitutive equationGeologyComposite materialStructural engineeringFinite element methodEngineering

Abstract

fetched live from OpenAlex

Clays exhibit creep in compression and shear. In one-dimensional compression, creep is commonly known as “secondary compression” even though it is also a significant component of deformations resulting from shear straining. It reflects viscous behaviour in clays and therefore depends on load duration, stress level, the ratio of shear stress to compression stress, strain rate, and temperature. Research described in the paper partitions strains into elastic (recoverable) and plastic (nonrecoverable) components. The plastic component includes viscous strains defined by a creep rate coefficient ψ that varies with plasticity index and temperature (T), but not with stress level or overconsolidation ratio (OCR). Earlier elastic–viscoplastic (EVP) models have been modified so that ψ = ψ(T) in a new elastic–thermoviscoplastic (ETVP) model. The paper provides a sensitivity analysis of simulated results from undrained (CIŪ) triaxial compression tests for normally consolidated and lightly overconsolidated clays. Axial strain rates range from 0.15%/day to 15%/day, and temperatures from 28 to 100 °C.

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: none
Teacher disagreement score0.988
Threshold uncertainty score0.827

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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

Explore more

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