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Record W2013095601 · doi:10.1139/t08-136

Modified state-surface approach to the study of unsaturated soil behavior. Part I: Basic concept

2009· article· en· W2013095601 on OpenAlexvenueno aff
Robert L. Lytton

Bibliographic record

VenueCanadian Geotechnical Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsYield surfaceGeotechnical engineeringHardening (computing)IsotropyStress pathSoil waterVoid ratioNonlinear systemMaterials scienceCritical state soil mechanicsPlasticityEffective stressMechanicsConstitutive equationGeologyComposite materialFinite element methodStructural engineeringSoil scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

The traditional state-surface approach to the study of unsaturated soil behavior is becoming much less popular these days, as it uses unique constitutive surfaces to represent unsaturated soil behavior. This approach is essentially a nonlinear elastic formation and cannot be used to explain complex stress-path dependency for unsaturated soils. In this paper, a modified state-surface approach (MSSA) is proposed to represent unsaturated soil behavior under isotropic stress conditions in which a conventional void-ratio state surface is considered to be made up of an elastic surface and a plastic hardening surface. The plastic hardening surface remains stationary at all times, whereas the elastic surface remains unchanged when the soil experiences elastic deformation and moves downward when there is plastic hardening occurrence. Using the MSSA, the loading–collapse (LC) and the suction increase (SI) yield curves in the Barcelona basic model (BBM) are derived. The prediction of three typical cases of soils under isotropic conditions and experimental results using the proposed approach confirmed its feasibility, simplicity, and potential for the study of unsaturated soil behavior.

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 score0.203
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.216
Teacher spread0.198 · 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

Citations61
Published2009
Admission routes1
Has abstractyes

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