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Record W3122181911 · doi:10.1115/ipc2020-9510

Dense and Sparse Stress Corrosion Crack Initiation in an X65 Pipeline Steel With Mill Scale

2020· article· en· W3122181911 on OpenAlexaff
Shidong Wang, Lyndon Lamborn, Karina Chevil, Erwin Gamboa, Weixing Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceCorrosionStress corrosion crackingPipeline transportCrackingComposite materialStress (linguistics)Crack closureFracture mechanicsMetallurgyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Near-neutral pH stress corrosion cracking (SCC) is a significant threat to the operational safety and reliability of gas and oil pipelines. The SCC cracks are typically formed in colonies with different crack density populations on the external surface of the pipe. The density of SCC cracks affect how pipeline integrity and remaining lifetime are assessed. Although sparse and dense crack colonies are commonly observed on pipelines, it has not been well established how these crack colonies with different crack populations were developed in the field. This research was made in an attempt to replicate near-neutral pH SCC cracks with different crack densities in the laboratory with realistic loading conditions commonly found during field operation. An X65 pipeline steel with different surface preparations was used. The results showed that the dense near-neutral pH SCC cracks were successfully reproduced on the primer-coated samples, whereas sparse cracks were reproduced on the mill-scaled and polished samples. The densely spaced cracks could transform into sparsely spaced cracks when most of the primer layer and mill scale had been removed during the long period of corrosion under cyclic loading and further corrosion occurred thinning the crack density. The results of crack initiation obtained from this investigation have also been found to be quite consistent with crack initiation scenarios found during field operation.

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 score0.011
Threshold uncertainty score0.680

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)0.0010.000

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 head0.032
GPT teacher head0.259
Teacher spread0.227 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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