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Record W2017112074 · doi:10.1115/ipc2010-31376

Considerations of Linepipe and Girth Weld Tensile Properties for Strain-Based Design of Pipelines

2010· article· en· W2017112074 on OpenAlexaff
Yong-Yi Wang, Ming Liu, J. A. Gianetto, Bill Tyson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsUltimate tensile strengthWeldingStrain hardening exponentMaterials sciencePipeline transportPipingGirth (graph theory)Tensile testingStructural engineeringHardening (computing)MetallurgyComposite materialEngineeringMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Pipelines in certain regions are expected to survive high longitudinal strains induced by seismic activities, slope instability, frost heave, and mine subsidence. Material properties, of both pipes and girth welds, are critical contributing factors to a pipeline’s strain capacity. These factors are examined in this paper with particular focus on the modern high strength pipes (grade X70 and above) usually made from microalloyed control-rolled TMCP steels. The examination of the tensile properties of pipes includes some of the most basic parameters such as yield strength, strength variation within a pipe, and newly emerging issues of strength and strain hardening dependence on temperature. The girth weld tensile properties, particularly yield strength, are shown to be dependent on the location of the test specimen. There are strong indications from the tested welds that strain hardening of the welds is dependent on test temperature. The effects of strain aging on pipe and girth weld properties are reviewed. This line of reasoning is extended to possible strain aging effects during field construction, although experimental evidence is lacking at this moment. The paper concludes with considerations of practical implementation of the findings presented in the early part of the paper. Recommendations are made to effectively deal with some of the challenging issues related to the specification and measurement of tensile properties for strain-based design.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.033
GPT teacher head0.230
Teacher spread0.197 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations2
Published2010
Admission routes1
Has abstractyes

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