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Modeling and Parametric Study of Gasketed Bell and Spigot Joint in Buried RC Pipeline

2019· article· en· W2929998994 on OpenAlexaff
David Becerril García, Ian D. Moore, Jacinto Cortés‐Pérez

Bibliographic record

VenueJournal of Pipeline Systems Engineering and Practice · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsQueen's University
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
KeywordsStructural engineeringJoint (building)StiffnessParametric statisticsExpansion jointRotation (mathematics)Shear (geology)Displacement (psychology)EngineeringPipeline (software)AxleGeotechnical engineeringGeologyComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Joints in buried pipelines have diverse configurations and materials, and are subjected to different loading and installation conditions which will influence their performance. Therefore these parameters need to be examined, because joints play an important role in pipeline longevity. This research study developed a numerical model for a buried RC pipeline with a gasketed bell and spigot joint subjected to surface loads representing service conditions. The model was calibrated against experimental data and captured close to 80% of the joint rotation angle and the patterns of hoop strain at the joint components. The model was subsequently used in parametric studies which revealed the locations where a single wheel pair produces the largest shear displacement and rotation of the joints for the burial depth examined. The studies also indicated that a tandem axle configuration produces larger joint rotation than a single axle and single wheel pair configuration. The influence of the soil envelope stiffness and the effect of pavements on the joint performance was also described.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.228
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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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