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Record W2066950702 · doi:10.1115/1.4029895

Effect of Operating Pressure and Dent Depth on Burst Strength of NPS30 Linepipe With Dent–Crack Defect

2015· article· en· W2066950702 on OpenAlexafffund
Hossein Ghaednia, Sreekanta Das, Rick Wang, Richard Kania

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)University of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WindsorUniversity of Calgary
KeywordsStructural engineeringMaterials scienceLeakPipeline (software)Hydrostatic testCorrosionPipeline transportComposite materialGeotechnical engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Buried linepipe can be exposed to various external interferences and corrosive environment and as a result, damage in the form of dent or corrosion or crack or gouge or combination of any of these damages can form in the pipe wall. A defect combining dent and crack, often known as dent–crack defect, which may lead to a rupture or leak in the pipe wall and hence, the pipeline operator becomes concerned about the performance and safety of the pipeline. A research was recently completed at the Centre for Engineering Research in Pipelines (CERP), University of Windsor to study the influence of dent depth and operating line pressure on the pressure capacity (burst strength) of 30 in. diameter and X70 grade linepipe. This study found that the dent depth of 12% with crack depth of 4 mm or more can reduce the pressure capacity by 38%. This paper discusses the test specimens, test setup, test procedure, test results, and data obtained from finite element analyses.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Citations29
Published2015
Admission routes2
Has abstractyes

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