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Record W2014102115 · doi:10.1115/ipc2004-0320

Fracture Toughness Testing of Pipeline Girth Welds

2004· article· en· W2014102115 on OpenAlexaff
G. Shen, J. A. Gianetto, R. Bouchard, J. T. Bowker, W. R. Tyson

Bibliographic record

Venue2004 International Pipeline Conference, Volumes 1, 2, and 3 · 2004
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGirth (graph theory)WeldingMaterials scienceShielded metal arc weldingFracture toughnessCrack tip opening displacementToughnessStructural engineeringPipeline (software)MetallurgyComposite materialFracture mechanicsEngineeringMechanical engineeringArc weldingMathematicsGas tungsten arc weldingCrack closure

Abstract

fetched live from OpenAlex

The guidelines and recommendations for fracture toughness testing of pipeline girth welds outlined in CSA Z662-03, Annex K are reviewed in this work. In Annex K of CSA Z662-03, the specimen type and notch location have been grouped into four categories and the CTOD tests are to be carried out in accordance with either BSI Standard 7448 or ASTM Standard E 1290. In the present study, CTOD tests have been conducted on a manual shielded-metal-arc weld (SMAW) that was prepared in a high strength X80 pipeline steel. The experimental results obtained by applying the two testing standards are compared. The focus was to identify the differences between these two standards that may significantly affect the test results, such as the requirements for straightness of the fatigue crack, and the equations and parameters used for evaluation of CTOD. Some additional factors affecting the testing, such as selection of test specimen location and procedures for targeting specific weldment microstructures as well as the application of local compression, are also discussed. The variation of strength and toughness with clock position around the circumference of the girth welds has also been studied.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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 teacher head, 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

Citations6
Published2004
Admission routes1
Has abstractyes

Explore more

Same venue2004 International Pipeline Conference, Volumes 1, 2, and 3Same topicFatigue and fracture mechanicsFrench-language works237,207