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Failure of an effective stress approach in polydisperse wet granular materials

2024· article· en· W4394568412 on OpenAlexafffund
David Cantor, Émilien Azéma, Carlos Ovalle

Bibliographic record

VenuePhysical Review Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsPolytechnique Montréal
FundersAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsGranular materialDiscrete element methodStress (linguistics)Relevance (law)Materials scienceGeotechnical engineeringMechanicsStatistical physicsComposite materialGeologyPhysicsPhilosophyLaw

Abstract

fetched live from OpenAlex

In this Letter, the effective stress principle (ESP)—originally developed for granular materials saturated with water and recently extended to unsaturated ones via simulations—is shown to fail once the material presents wide grain size distributions. We demonstrate that the current ESP approaches cannot capture the Mohr-Coulomb strength parameters as soon as the grain size span exceeds <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:msub><a:mi>d</a:mi><a:mi>max</a:mi></a:msub><a:mo>/</a:mo><a:msub><a:mi>d</a:mi><a:mi>min</a:mi></a:msub><a:mo>∼</a:mo><a:mn>4</a:mn></a:mrow></a:math>. This failure is attributed to significant differences in the fabric generated by solid interactions in the wet material, which are supposedly capable of matching the characteristics of the dry material. We show that a generalization of the ESP requires not only macroscopic considerations but also direct attention to the nature of contact and force networks. Published by the American Physical Society 2024

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.361
Teacher spread0.341 · 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 designBench or experimental
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

Citations3
Published2024
Admission routes2
Has abstractyes

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