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Record W3111817253 · doi:10.1002/pen.25603

Revisiting creep test on polyethylene pipe—Data analysis and deformation mechanisms

2020· article· en· W3111817253 on OpenAlexafffund
P.‐Y. Ben Jar

Bibliographic record

VenuePolymer Engineering and Science · 2020
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCreepMaterials scienceDeformation (meteorology)Stress (linguistics)Amorphous solidHydrostatic stressTerm (time)Hydrostatic equilibriumComposite materialPhase (matter)Diffusion creepForensic engineeringMechanicsStructural engineeringMicrostructureCrystallographyEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Creep tests have long been used to determine long‐term hydrostatic strength (LTHS) for polyethylene (PE) pipe. In view that current standard requires some long‐term creep tests of over 9000 h to determine LTHS, this paper explores the possibility of using relatively short‐term creep tests for the same purpose. The study found that trend line for applied stress versus failure time changes at the failure time around 10 h, and the data trend in the low‐stress regime is consistent with that from the standard test. The trend line change is believed to be caused by the change of the involvement of amorphous and crystalline phases in PE in the deformation process, that is, sequential involvement of the amorphous phase first and then the crystalline phase in the low‐stress regime, and simultaneous involvement of both phases from the beginning in the high‐stress regime. Activation energies based on the Eyring's model and the Norton power law are determined to examine the proposed concept. Both models suggest that additional activation energy is needed for deformation in the high‐stress regime, which provides some support to the proposed concept and the possibility of using short‐term creep tests to determine LTHS.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.236
Teacher spread0.215 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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