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Record W2149010427 · doi:10.1111/ffe.12274

Underload‐induced crack growth behaviour of minor cycles of pipeline steel in near‐neutral pH environment

2014· article· en· W2149010427 on OpenAlexafffund
Mengshan Yu, W. Chen, Richard Kania, G. Van Boven, Jenny Been

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2014
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsSpectra Energy (Canada)TransCanada (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePipeline (software)Stress corrosion crackingMinor (academic)CrackingStress (linguistics)AmplitudeCorrosionMetallurgyStructural engineeringComposite materialEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Despite decades of research, the mechanisms of near‐neutral pH stress corrosion cracking of pipeline steel are still not fully understood. This investigation was aimed to understand the effect of minor cycles with very high R ratios (minimum stress/maximum stress) on crack growth in air and in near‐neutral pH environments. It has been demonstrated that the minor cycle, even with an R ratio as high as 0.9, could significantly contribute to the crack growth of pipeline steels in the presence of a large underload cycle with a low R ratio of 0.5 in near‐neutral pH environment. Comparing with constant amplitude tests, an increase of crack growth rate by a factor of 3 and 5 was observed when some non‐propagating minor cycles were combined with an underload cycle for tests in air and in near‐neutral pH solution, respectively. Based on the test results, the crack growth mechanisms during minor cycle loading in near‐neutral pH environments were discussed and practical strategies aimed to minimise crack growth during pipeline operation were also proposed.

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 categoriesMeta-epidemiology (narrow)
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 score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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.

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

Citations21
Published2014
Admission routes2
Has abstractyes

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