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Record W4412737136 · doi:10.1016/j.compgeo.2025.107512

An extended yield surface model for predicting the elastoplastic behaviours of unsaturated soils

2025· article· en· W4412737136 on OpenAlexafffund
Yao Li, Sai K. Vanapalli

Bibliographic record

VenueComputers and Geotechnics · 2025
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilUniversity of Ottawa
KeywordsYield surfaceYield (engineering)Soil waterSurface (topology)Geotechnical engineeringMaterials scienceGeologyMathematicsSoil scienceStructural engineeringFinite element methodGeometryEngineeringComposite materialConstitutive equation

Abstract

fetched live from OpenAlex

Several investigators have proposed constitutive relationships in the literature for modelling the elastoplastic behaviours of unsaturated soils extending either the effective stress concept or the independent stress state variables approach. However, a unified model that can be used for predicting various types of loading collapse curves for unsaturated soils is still lacking in the literature. To address this objective, a loading collapse model that can be expressed extending either the effective stress concept or the independent stress state variables approach is proposed in this paper. The proposed model is flexible that can model various types of loading collapse curves for unsaturated soils. More importantly, the model has clear physical meanings of parameters that are consistent with the classical terminology used in literature. The performance of the proposed model is validated using the published experimental results from the literature considering different scenarios. The proposed elastoplastic framework is a promising tool for modelling the complex hydro-mechanical behaviours of unsaturated soils.

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 score0.488
Threshold uncertainty score0.409

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 integrity0.0000.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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