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Record W2016888431 · doi:10.3141/2131-11

Analysis of a Gasketed Polyvinyl Chloride Pipe Joint

2009· article· en· W2016888431 on OpenAlexafffund
Müge Balkaya, Ian D. Moore

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2009
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsGasketJoint (building)Polyvinyl chlorideFinite element methodStructural engineeringNatural rubberPipeline transportMaterials scienceModulusComposite materialEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Poor performance of pipe joints can contribute significantly to reduction of the overall performance of pipe systems. The most common problems attributed to joint defects are infiltration, exfiltration, and erosion of the soil surrounding the pipe, which can ultimately produce pipe failure. Thus, modeling of joints in pipelines is a critical issue influencing both the short- and long-term performance of these systems. Although joints may often be the weakest points along the pipe and can have a significant influence on pipe performance, little research has been conducted with regard to joint design. Even joint response to simple loading conditions is largely unknown because of the complexity of interactions between the pipes and the gasket. A three-dimensional, finite element analysis of a Rieber-type rubber-gasketed polyvinyl chloride (PVC) water pipe joint was carried out to investigate the influence of gasket modulus, rubber–PVC friction, insertion length, and joint rotation on the overall performance of the pipe joint system. The numerical analyses were performed with ABAQUS.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.043
GPT teacher head0.319
Teacher spread0.276 · 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 designObservational
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

Citations22
Published2009
Admission routes2
Has abstractyes

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