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Record W1973163126 · doi:10.1115/ipc2010-31007

Frequency Dependence of Fatigue and Corrosion Fatigue Crack Growth Rate

2010· article· en· W1973163126 on OpenAlexafffund
Mohammad Marvasti, Weixing Chen, Richard Kania, Robert Worthingham, Greg Van Boven

Bibliographic record

Venue2010 8th International Pipeline Conference, Volume 1 · 2010
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Failure Mechanisms
Canadian institutionsSpectra Energy (Canada)TransCanada (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Transportation
KeywordsParis' lawMaterials scienceCorrosion fatigueCorrosionCrack closureGrowth rateHydrogenPipeline transportMetallurgyCreepComposite materialFracture mechanicsEnvironmental scienceChemistry

Abstract

fetched live from OpenAlex

Corrosion fatigue and fatigue crack growth in air tests were comparatively conducted on an X52 pipelines steel. Fatigue crack growth rates in air were lower than corrosion fatigue crack growth rates due to the absence of hydrogen and mechanical dormancy arisen from low temperature creep at low cyclic frequencies. Mechanical dormancy can commonly occur during operation of both oil and gas pipelines. Crack growth in near neutral pH environments can be well rationalized by a combined loading factor, (ΔK)2Kmax/fα, which reflects the synergistic interaction between the mechanical driving force and the hydrogen effects. Hydrogen plays a decisive role in terms of crack growth in pipelines steels exposed to near neutral pH environments.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.255
Teacher spread0.226 · 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 designNot applicable
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

Citations3
Published2010
Admission routes2
Has abstractyes

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