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Record W2321642097 · doi:10.1061/9780784413272.280

Numerical DEM Examination of a Torsional Shear Test

2014· article· en· W2321642097 on OpenAlexaff
Bo Li, Fengshou Zhang, Marte Gutierrez

Bibliographic record

VenueGeo-Congress 2014 Technical Papers · 2014
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsGeomechanica (Canada)
FundersNatural Science Foundation of Zhejiang Province
KeywordsShear (geology)Shear stressDirect shear testMaterials scienceShear bandPorosityShear rateBoundary element methodDiscrete element methodTriaxial shear testFinite element methodShear wallGeotechnical engineeringStructural engineeringShear zoneMechanicsGeologyComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

The paper presents preliminary results of three-dimensional simulation of the pure torsional shear (PTS) test using the discrete element method with the stacked-wall technique to simulate the boundary conditions in the test. The average stress and strain components in the PTS test are presented. To investigate the stress-strain relationship inside and outside shear bands that develop during the loading, a measuring sphere is employed to monitor the localization behavior of the specimen. The evolution of porosity and coordination number in the shear zone at different locations is presented. The initiation and propagation of the shear band characterized by porosity and shear rate contour are visualized, which indicated the shear rate is a better indicator for shear-banding identification. The results demonstrate also that, based on the microparameter monitoring, the stress and strain measured in the shear zone is significantly different from boundary measurement. The paper also demonstrates that the discrete element method is an effective tool to investigate the mechanical behavior of particular materials.

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 categoriesInsufficient payload (model declined to judge)
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 score0.358
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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.

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

Citations1
Published2014
Admission routes1
Has abstractyes

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