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Record W2629701509 · doi:10.1002/nag.2708

Coupling of solid deformation and pore pressure for undrained deformation—a discrete element method approach

2017· article· en· W2629701509 on OpenAlexafffund
Yifei Cui, Dave Chan, Alireza Nouri

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDiscrete element methodPore water pressureIsotropyExtended discrete element methodDeformation (meteorology)MechanicsMaterials scienceCoupling (piping)Geotechnical engineeringParticle (ecology)PorosityFinite element methodEffective stressIsotropic solidContact mechanicsStress (linguistics)Solid mechanicsStructural engineeringComposite materialEngineeringGeologyExtended finite element methodPhysicsFinite element limit analysis

Abstract

fetched live from OpenAlex

Summary This paper presents a numerical scheme for fluid‐particle coupling that uses the discrete element method by taking into consideration solid deformation and pore pressure generation. A new water particle element is introduced to calculate pore water pressure due to porosity changes. The water particle element has the same size and shape as the solid element and experiences the same amount of deformation. On the basis of the effective stress principle at the element contact, the total force is equal to the sum of the force transmitted through the solid element contact and the water particle force due to pore water pressure. Analytical solutions of traditional soil mechanics problems, such as isotropic compression and consolidated triaxial undrained test, are used to quantitatively validate the proposed model. The numerical results show good agreement between the model and the analytical solutions. The model therefore provides an effective method to calculate pore pressure in a porous medium in discrete modeling.

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)0.002
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.433
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.028
GPT teacher head0.371
Teacher spread0.343 · 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
GenreMethods

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

Citations64
Published2017
Admission routes2
Has abstractyes

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