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Record W4409037654 · doi:10.1007/s11440-025-02600-3

The mechanical behaviour of gap-graded soils under triaxial compression and extension loading paths

2025· article· en· W4409037654 on OpenAlexaff
Peter Adesina, Antoine Wautier, Nadia Benahmed

Bibliographic record

VenueActa Geotechnica · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersAgence Nationale de la Recherche
KeywordsSolid mechanicsExtension (predicate logic)Geotechnical engineeringMaterials scienceCompression (physics)Soil waterStructural engineeringGeologyComposite materialEngineeringComputer scienceSoil science

Abstract

fetched live from OpenAlex

The influence of mechanical loading paths on the characteristics of gap-graded granular assemblies was investigated using the discrete element method (DEM). Dense and loose gap-graded assemblies with finer fraction content, $${f}_{c}$$ , ranging from 0–100% were prepared and subjected to drained triaxial compression and extension loading paths. After examining key macroscale quantities, micromechanical analyses were conducted to elicit the particle-scale characteristics including the evolution of the fabric of the assemblies under the different loading paths. The results of the DEM analysis confirm the validity of the Mohr–Coulomb failure criteria at the critical state. While the mobilised friction angle at the peak is higher under extension than in compression, no significant difference was obtained in the critical state friction angle for both loading paths. Despite the higher mean stress transmitted by the gap-graded assemblies under compression in comparison with extension, the contribution of the finer particles to the total mean stress is not significantly influenced by the loading paths. Our data show that the variation in the fabric of granular assemblies under different loading paths does not always stem from an initial inherent anisotropy. Fabric anisotropy is marginally higher under extension than in compression despite having an initial isotropic fabric.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.708

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 integrity0.0000.001
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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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 routes1
Has abstractyes

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