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Record W4379617220 · doi:10.1139/cgj-2022-0348

Accounting for the microstructure for the prediction of unsaturated shear strength of remolded fine-grained soils

2023· article· en· W4379617220 on OpenAlexvenueno aff
Régis Mpawenayo, Pierre Gérard

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringMicrostructureSoil waterEffective stressShear strength (soil)Mercury intrusion porosimetryMaterials scienceShear (geology)MacroporeGeologyComposite materialPorositySoil sciencePorous medium

Abstract

fetched live from OpenAlex

Predictions of the unsaturated shear strength with generalized effective stress-based approaches disregard the non-uniform microstructure of remolded fine-grained soils. The study aims at investigating the adequacy of microstructurally-based effective stress to predict the unsaturated shear strength of remolded fine-grained soils over a wide range of suctions. For that purpose, shear strength data are acquired on a silty clay soil compacted at two different dry densities through suction-controlled triaxial tests and unconsolidated triaxial tests at constant water content. The microstructure of the soil at the as-compacted state is determined with mercury intrusion porosimetry and is directly incorporated in different expressions of microstructurally-based effective stress available in the literature. The experimental data suggest that compared to the generalized effective stress, microstructurally-based effective stress expressions provide better predictions of the unsaturated shear strength, especially at high suctions. Also, the use of microstructurally-based effective stress is particularly relevant for remolded fine-grained soils compacted at high dry densities, i.e., with a low proportion of macropores.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.667
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.205
Teacher spread0.194 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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