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Record W4221091971 · doi:10.1016/j.compgeo.2022.104695

Fabric response to stress probing in granular materials: Two-dimensional, anisotropic systems

2022· article· en· W4221091971 on OpenAlexafffund
Chaofa Zhao, N. P. Kruyt, Mehdi Pouragha, Richard Wan

Bibliographic record

VenueComputers and Geotechnics · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of CalgaryCarleton University
FundersH2020 Marie Skłodowska-Curie ActionsHorizon 2020Natural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsPlane (geometry)Plane stressAnisotropyMaterials scienceTensor (intrinsic definition)Stress (linguistics)MicrostructureCauchy stress tensorInfinitesimal strain theoryGeometryComposite materialFinite element methodMechanicsStructural engineeringPhysicsMathematicsOpticsClassical mechanicsEngineering

Abstract

fetched live from OpenAlex

The microstructure of granular materials has a significant influence on their macroscopic quasi-static strength and deformational behaviour. This microstructure is often quantified by a second-order fabric tensor that describes the primary orientational statistics of interparticle contacts. Here, it is investigated how the fabric tensor changes when samples are subjected to small (strain) loadings with different ‘directions’, i.e. probes. This is accomplished by the analysis of extensive sets of Discrete Element Method (DEM) simulations for various anisotropic, pre-peak two-dimensional samples, where both in-plane (i.e. coaxial with the current stress and fabric tensor) and out-of-plane, noncoaxial probes are considered. The results of DEM simulations show that the in-plane and out-of-plane fabric responses are effectively decoupled, i.e. they are only dependent on the in-plane and out-of-plane strain increment, respectively. The out-of-plane fabric increment is proportional to the out-of-plane strain increment whereas the in-plane fabric increment is linearly dependent on the in-plane strain increment. An accurate theoretical description (with a modest number of model parameters) has been developed that describes the fabric response to the imposed, in-plane as well out-of-plane, strain increments for the considered systems.

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 score0.032
Threshold uncertainty score0.800

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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

Citations22
Published2022
Admission routes2
Has abstractyes

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