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Record W4409502120 · doi:10.5006/c2022-17682

External Stress Corrosion Cracking of In-Situ Carbon Steel Pipelines

2022· article· en· W4409502120 on OpenAlexaff
Duane Serate, Matthew Krantz, Yashar Behnamian, Haixia Guo, Simon Yuen, Victor Itulua

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsAlberta InnovatesSuncor Energy (Canada)
Fundersnot available
KeywordsStress corrosion crackingMaterials scienceCarbon steelCrackingPipeline transportCorrosionMetallurgyStress (linguistics)Carbon fibersIn situComposite materialEnvironmental scienceChemistryComposite number

Abstract

fetched live from OpenAlex

Abstract There has been multiple reported and documented external stress corrosion cracking (SCC) of above-ground carbon steel pipelines within the past 5 years for a number of in-situ oilsands operators. Majority of these failures were on pipeline carbon steel grades API 5L or Z245.1 with specified minimum yield strength (SMYS) 52 ksi or higher, but there were also reports of external SCC on A106 Gr B and A234 WPB steel grades. All the cracks manifested on the bare (uncoated) Outside Diameter (OD) surface of the pipe, and all were associated with wet mineral wool insulation. Common features include operating temperature between 70°C and 160°C, intergranular cracking morphology, and exposed to wet mineral wool insulation with vintage post 2005. Previously, SCC of carbon steel pipelines has been commonly reported and investigated for buried pipelines. There is limited information in the industry however on SCC of above ground carbon steel pipelines. Extensive field and lab tests were performed to understand the mechanism, in conjunction with technical studies/literature reviews. This paper provides a summary aimed at increasing the awareness of the industry with this new damage mechanism, and improve public safety.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.021
GPT teacher head0.273
Teacher spread0.253 · 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 designObservational
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

Citations4
Published2022
Admission routes1
Has abstractyes

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