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Record W2944922339 · doi:10.24423/aom.3084

State-dependent fractional plasticity model for the true triaxial behaviour of granular soil

2018· article· en· W2944922339 on OpenAlexaff
Yifei Sun, Wojciech Sumelka

Bibliographic record

VenueOpen MIND · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsPlasticityStress spaceGranular materialFractional calculusTriaxial shear testMathematicsGeotechnical engineeringStress (linguistics)Flow (mathematics)Soil waterMechanicsMaterials scienceMathematical analysisGeometryPhysicsGeologyConstitutive equationThermodynamicsFinite element methodComposite materialShear (geology)Soil science

Abstract

fetched live from OpenAlex

The fractional plasticity was proposed to model the stress-strain behaviour of granular soils, but only within the scope of classical triaxial loading condition. In this study an attempt is made to develop a 3D fractional plasticity model for granular soils subjected to true triaxial loads by using characteristic stress, where all the fractional-order and integer-order derivatives can be easily obtained. Without using a plastic potential, the non-associated plastic flow rule is achieved by performing fractional derivatives of the yielding function in the characteristic stress space. The obtained plastic flow direction is found to be influenced by the fractional order, characteristic stress parameter and intermediate stress ratio. To further validate the proposed model, a series of true triaxial test results of different granular soils are simulated, from which good agreement between the model predictions and the corresponding test results is found.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.265
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2018
Admission routes1
Has abstractyes

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