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Record W2042854405 · doi:10.2136/vzj2013.06.0113

Micromechanical Analysis of Force Transport in Wet Granular Soils

2014· article· en· W2042854405 on OpenAlexafffund
Richard Wan, S. Khosravani, Mehdi Pouragha

Bibliographic record

VenueVadose Zone Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapillary actionIsotropyGeotechnical engineeringStress (linguistics)AnisotropySaturation (graph theory)Materials scienceGranular materialEffective stressWater contentDiscrete element methodMechanicsMoistureComposite materialGeologyMathematicsPhysicsOptics

Abstract

fetched live from OpenAlex

This study investigated the mechanical behavior of a wet granular soil at low moisture content in which isolated pendular water bridges (menisci) at the interface of the solid particles give rise to capillary forces in addition to existing interparticle contact forces. We derived a single effective stress tensor encapsulating evolving liquid bridges, packing, interfaces, and water saturation. Apart from the fact that the stress due to contact forces is dependent on the fabric, we found that the so‐called suction (capillary) stress arising from liquid bridges is inevitably direction dependent, i.e., anisotropic. The latter is at odds with the common belief that capillary stress is isotropic. We demonstrate that capillary stress is a function of the distribution of liquid bridges, degree of saturation, as well as particle packing, and thus provide an adequate effective stress definition to describe both the constitutive behavior and strength of unsaturated media. We conducted discrete element modeling simulations of triaxial compression tests of pendular‐state granular samples at different matric suctions to verify the anisotropic nature of the capillary stress and resulting strength contributions to validate the proposed effective stress equation.

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 score0.226
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.198
Teacher spread0.192 · 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

Citations37
Published2014
Admission routes2
Has abstractyes

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