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Record W1974104317 · doi:10.4043/23779-ms

Pipeline Mechanical Damage Assessment Using Finite Element Methods

2012· article· en· W1974104317 on OpenAlexaff
W. Hanif, Shawn Kenny

Bibliographic record

VenueOTC Arctic Technology Conference · 2012
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFinite element methodPipeline transportStructural engineeringPipeline (software)Submarine pipelineStructural integrityComputer scienceMarine engineeringWeldingEngineeringEnvironmental scienceForensic engineeringMechanical engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Onshore and offshore pipelines may be subjected to mechanical damage duringinstallation and operation due to environmental loads, external forces andthird parties. The type and severity of pipe damage may influence operational, repair and intervention strategies. For conventional pipelines, the assessmentof mechanical damage plays a role in the development of integrity managementprograms that can be of greater significance for pipeline systems located inremote, harsh environments. The current study highlights the effect of plaindents and interaction of plain dents with girth weld on pipe mechanicalresponse using continuum finite element methods. The modelling procedures arecalibrated with available physical datasets and also demonstrate excellentcorrelation with third party simulations. Confidence in the numericalsimulation tool provides a basis to evaluate the effects of mechanical damagethrough a broader parameter study and assess effects on fatigue lifeperformance.

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)0.001
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.345
Teacher spread0.305 · 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
Published2012
Admission routes1
Has abstractyes

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