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Record W2067662840 · doi:10.1115/omae2013-10556

Development of a Fatigue Life Assessment Tool for Pipelines With Local Wrinkles

2013· article· en· W2067662840 on OpenAlexafffund
Fahad Bakhtyar, Shawn Kenny

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsBuckleStructural engineeringPipeline transportWrinkleDeformation (meteorology)BucklingParametric statisticsPipeline (software)Displacement (psychology)EngineeringMaterials scienceForensic engineeringMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

Pipelines may be subject to ground movement events or external interference that imposes axial and moment loading into the pipeline. This system demand may cause localized deformation mechanisms to develop, that may be observed as local wrinkling or buckling of the pipe wall. The local buckle amplitude may increase with continued external loading and may fracture due to low cycle fatigue failure caused by operational conditions. There exists limited data and engineering guidance on the mechanical performance of energy pipelines with a local wrinkle or buckle. The literature suggests the fatigue service life can be significantly reduced by the presence of local wall deformation mechanisms. In this study, continuum numerical modelling procedures are developed to assess the influence of pipeline damage in the form of a local wrinkle or buckle on the low-cycle fatigue life. The simulation tool is calibrated from the available literature and a parametric study is conducted to examine the influence of wrinkle bend radius, pipe wall thickness, cyclic displacement amplitude, material grade and constitutive models on the pipe mechanical 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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.256
Teacher spread0.237 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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