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Record W3125260291 · doi:10.1115/ipc2020-9706

A Quantitative Index to Assess the Influence of Joint Fit-Up on Pipeline Weld Root Discontinuities

2020· article· en· W3125260291 on OpenAlexaff
Mitchell R. Grams, Luke Ludwig, Patricio F. Méndez

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2020
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClassification of discontinuitiesWeldingOffset (computer science)Pipeline transportStructural engineeringDiscontinuity (linguistics)Materials scienceEngineeringMechanical engineeringComputer scienceMathematicsComposite materialMathematical analysis

Abstract

fetched live from OpenAlex

Abstract Field experience on pipelines suggests that under the unique conditions of tie-in welding, a high-low offset at the inner pipe wall is related to an increased occurrence of latent root weld discontinuities such as cold cracking. Codes and standards offer conflicting and unclear guidelines regarding acceptance criteria for high-low offset. This study presents a numerical index to quantify the influence of non-ideal joint geometry on the latent discontinuity susceptibility of the root pass for circumferential pipeline welds. The index is based on the stress concentration at the root and the angular distortion associated with plastic strains produced during welding. This index relates geometric considerations such as pipe diameter, wall thickness, and the cross section of the root pass with welding procedure variables and the mechanical properties of the pipeline material. Although this study is meant for steel pipelines, the conclusions obtained are also applicable to other materials. The index presented is a contribution towards an objective criterion for acceptance of high-low offset during field welding, ranking the susceptibility to latent discontinuities as a function of variables available to practitioners during field welding.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.494

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 head0.044
GPT teacher head0.274
Teacher spread0.231 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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