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Record W2515328445 · doi:10.1061/9780784480151.059

A Methodology to Determine the Viscoplastic Perzyna Model Parameters for Granular Materials under High Strain Rates

2016· article· en· W2515328445 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGeo-Chicago 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsUniversity of Toronto
FundersPublic Works and Government Services CanadaGovernment of Canada
KeywordsViscoplasticitySplit-Hopkinson pressure barConstitutive equationGranular materialStrain rateMaterials scienceQuasistatic processNonlinear systemStrain (injury)Bar (unit)Geotechnical engineeringStructural engineeringFinite element methodComposite materialEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Although Perzyna’s theory of viscoplasticity has been accepted and used widely, there is no proper and unified method to determine this model’s parameters for granular materials such as sand under high strain rate conditions. To study these parameters, both static and dynamic tests have been conducted on Ottawa sand. The method used to determine the physical behavior of sand at high strain rates was the split Hopkinson pressure bar (SHPB) method. Experimental stress-strain results from granular soils ranging from strain rates of 200 s-1 to 2100 s-1 are presented in this paper. The constitutive equations are calibrated against the experimental data using the Marquardt-Levenberg nonlinear optimization algorithm.

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)0.001
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 score0.498
Threshold uncertainty score0.716

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)0.0000.000

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 head0.044
GPT teacher head0.283
Teacher spread0.239 · 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