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Discrete-Element Method Investigation of the Resilient Behavior of Granular Materials

2004· article· en· W2083323510 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Transportation Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDiscrete element methodGranular materialTriaxial shear testShear modulusGeotechnical engineeringMaterials scienceAggregate (composite)ModulusShear (geology)Contact forceContact dynamicsStructural engineeringMechanicsEngineeringComposite materialClassical mechanicsPhysics

Abstract

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This paper presents the results of numerical simulations of the resilient modulus test used to mechanically characterize the resilient behavior of aggregate materials, commonly used in pavement bases and subbases. The investigation made use of the discrete-element method (DEM) to replicate the particle behavior usually experienced during laboratory sample preparation and testing. The simulations were based on assemblies of circular particles confined between top and bottom rigid boundaries and laterally confined at constant stress. Contact forces and displacements were assumed to obey a linear relationship and shear forces were bounded by a maximum value (Coulomb friction law). Compacted samples were subjected to deviator repeated loads. The investigation showed that the DEM is capable of reproducing the results of the resilient modulus test performed on real granular materials in a qualitative manner. Further, the method predicted the effect of the state of stress depicted by laboratory testing.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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