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Record W2897671216 · doi:10.1520/jte20170419

Leakage Performance of Joints in Gravity Flow Pipes Subjected to Shear Force, Angular Misalignment, and Diameter Deformation

2018· article· en· W2897671216 on OpenAlexaff
David Becerril García, Ian D. Moore

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsPipeline transportLeakage (economics)Structural engineeringMaterials scienceInternal pressureGeotechnical engineeringShear (geology)Hydrostatic testEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Current standards for quality control testing of joints in gravity flow pipelines specify tests that do not evaluate conditions that have been registered in full-scale laboratory tests and are estimated using simplified design equations, particularly the shear forces transferred across the joint. In this research study, the leakage performance of joints in reinforced concrete, corrugated high-density polyethylene, and corrugated steel pipelines is evaluated when joints are subjected to expected service conditions. To do this, a testing apparatus that is capable of applying expected demands in a controlled fashion while the pipeline is subjected to internal or external fluid pressures was developed. It was observed that shear force and diameter changes controlled the leakage resistance of those joints, and that some were more susceptible to leakage when subjected to internal pressure compared to external pressure. The results are used to establish recommendations regarding test procedures of joints for culverts and other gravity flow applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2018
Admission routes1
Has abstractyes

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