MétaCan
Menu
Back to cohort
Record W2972823063 · doi:10.1002/peng.20184

Multi‐Relaxation Test to Characterize PE Pipe Performance

2019· article· en· W2972823063 on OpenAlexaff
Na Tan, P.‐Y. Ben Jar

Bibliographic record

VenuePlastics Engineering · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceFracture (geology)CreepDeformation (meteorology)BrittlenessRelaxation (psychology)Stress relaxationStress (linguistics)Composite materialHydrostatic stressForensic engineeringStructural engineeringEngineeringFinite element method

Abstract

fetched live from OpenAlex

Abstract A new concept is proposed, which uses results from a multi‐relaxation test to characterize transition of deformation mechanisms in polyethylene (PE) pipes, for plastic deformation from the amorphous phase only to the involvement of the crystalline phase. The former mechanism is believed to lead to brittle fracture, while the latter to ductile fracture. This phenomenon is believed to be related to the transition from ductile to brittle (DB) fracture that has been observed in creep tests of PE pipes by reducing the applied stress below a critical level. This paper presents results from 6 PE pipes of different density and molecular weight distribution. The results suggest that high‐density PE pipes require a higher deformation level for the DB transition than the medium‐density PE pipes. The results also suggest that the trend of change in the critical stress level for the DB transition is close to the trend of change in the hydrostatic design base, but the former takes less than two weeks to complete, while the latter more than 1 year. Therefore, the multi‐relaxation test can be used as an alternative method to characterize PE pipe performance, as a means for preliminary screening or in‐service monitoring of pipe performance.

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 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 score0.457
Threshold uncertainty score0.999

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)0.0000.001

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 head0.013
GPT teacher head0.187
Teacher spread0.174 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePlastics EngineeringSame topicPolymer crystallization and propertiesFrench-language works237,207