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Record W1999091315 · doi:10.1680/gein.15.00003

A novel viscoplastic model of high-density polyethylene pipe material

2015· article· en· W1999091315 on OpenAlexaff
M. S. A. Siddiquee, Ashutosh Sutra Dhar

Bibliographic record

VenueGeosynthetics International · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsViscoplasticityStrain rateMaterials scienceTangent modulusStress (linguistics)MechanicsStrain energy density functionFinite element methodBoundary value problemTangentConstitutive equationStructural engineeringComposite materialMathematicsMathematical analysisGeometryEngineeringPhysicsElastic modulus

Abstract

fetched live from OpenAlex

ABSTRACT: A nonlinear three-component elastic viscoplastic strain-rate-dependent model was developed for a high-density polyethylene pipe material. In this model, the stress at any point is assumed to be a unique function of the instantaneous irreversible strain, its rate and acceleration, irrespective of any intermediate stress and strain history. However, a reference strain rate was assumed and below this the response was independent of the strain rate. Parameters of the model were determined using published data on strain-rate-dependent material responses. The model was developed within the elasto-viscoplastic framework so that it could be used in solving boundary value problems. The return mapping algorithm of strain-rate-dependent elasto-plastic material is described with the detailed derivation of the consistent tangent operator for quadratic convergence. The model was successfully implemented in an existing finite-element code. Finite-element analyses using this model were capable of simulating the strain-rate-dependent stress–strain relation, the response to a jump in the strain rate and an unloading–reloading response for a high-density polyethylene pipe material published in the literature.

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: none
Teacher disagreement score0.527
Threshold uncertainty score0.514

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 head0.014
GPT teacher head0.207
Teacher spread0.193 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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