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Record W2129555886 · doi:10.1139/t10-061

Volume–mass unsaturated soil constitutive model for drying–wetting under isotropic loading–unloading conditions

2011· article· en· W2129555886 on OpenAlexvenueno aff
Hung Q. Pham, D. G. Fredlund

Bibliographic record

VenueCanadian Geotechnical Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutive equationGeotechnical engineeringSoil waterIsotropyCompressibilityVoid ratioDegree of saturationWettingSaturation (graph theory)Volume (thermodynamics)MechanicsGeologyMaterials scienceSoil scienceThermodynamicsFinite element methodMathematicsComposite materialPhysics

Abstract

fetched live from OpenAlex

A rigorous volume–mass constitutive model is proposed for the representation of drying–wetting under isotropic loading–unloading conditions for unsaturated soils. The proposed model utilizes concepts arising from soil physics and geotechnical engineering research and requires readily obtainable soils data for soil properties. The model can be used to predict void ratio and water content constitutive relationships (and therefore degree of saturation) for a wide range of unsaturated soils. Various stress paths (i.e., loading–unloading and drying–wetting) can be simulated, and hysteresis associated with the soil-water characteristic curve is taken into account. Two closed-form equations for the volume–mass constitutive relationships are presented for soils starting from slurry conditions. A number of test results (i.e., from experimental programs reported in the research literature) were used during the verification of the proposed volume–mass constitutive model. The volume–mass constitutive model captures key unsaturated soil conditions such as air-entry value, water-entry value, and residual conditions. The proposed model appears to satisfactorily predict unsaturated soil behavior for soils ranging from low compressible sands to high compressible clays.

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 categoriesMeta-epidemiology (narrow)
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 score0.837
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.027
GPT teacher head0.218
Teacher spread0.191 · 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

Citations28
Published2011
Admission routes1
Has abstractyes

Explore more

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