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Record W4405360590 · doi:10.1115/ipc2024-133962

Full-Scale Testing of Circumferential Stress-Corrosion Cracking Features in Pipelines: Bridging the Gap in Integrity Management

2024· article· en· W4405360590 on OpenAlexaff
Curtis Mokry, Kalen Jensen, Mark Brimacombe, Qishi Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsATCO (Canada)
Fundersnot available
KeywordsBridging (networking)Integrity managementStress corrosion crackingMaterials sciencePipeline transportCrackingCorrosionStructural engineeringStructural integrityStress (linguistics)Forensic engineeringComposite materialComputer scienceEngineeringMechanical engineeringComputer security

Abstract

fetched live from OpenAlex

Abstract Circumferentially oriented crack-like features, particularly those associated with circumferential stress-corrosion cracking (C-SCC), have emerged as a rising integrity challenge for pipeline operators. Despite considerable advancements in testing and model validation for axially oriented features, there remains a notable gap in understanding the load-carrying capacity of pipe with circumferentially oriented crack-like features. To address this knowledge deficit and enhance integrity management understanding around circumferential cracking, a Joint Industry Project (JIP) was undertaken with 10 pipeline operators. The JIP’s second phase was recently completed, which involved comprehensive full-scale testing of pipes with both machined circumferential features and natural C-SCC features from excavated pipe sections that were donated by JIP members. The primary aim of Phase 2 of the JIP was to contribute to the integrity management of circumferential crack-like flaws identified during in-line inspection, supporting decisions such as selecting excavation sites, planning mitigation measures, determining monitoring actions, and predicting failure loads. The JIP’s focus was on assessing the load-carrying capacity of pipe affected by C-SCC in a biaxial load-state (i.e. internal pressure and axial load) by evaluating the impact of flaw dimensions, internal pressure, and natural flaws (in comparison to machined ones). The full-scale test data was also used to evaluate existing failure prediction models (see IPC2024-133091). Testing of various flaw depths and length-to-depth ratios showed a maximum axial load reduction of 35% compared to the predicted failure load of pristine pipe. There was a minimal reduction in failure load between hoop stresses of 30% and 50% specified minimum yield stress (SMYS), and a moderate reduction between 50% and 72% SMYS when assessing the effect of internal pressure. Pipes that had severe natural flaws (up to 94% of wall thickness) were still able to carry loads that were considerably higher than predicted. Additionally, burst testing confirmed that circumferentially oriented crack-like features still maintain considerable burst capacity. This paper provides a comprehensive overview of the executed test program, including the outcomes and findings. The investigation scrutinizes the impact of flaw dimensions, internal pressure, and the distinction between natural and machined flaws on the load-carrying capacity of the tested pipes. As pipeline integrity remains a paramount concern, the outcomes of this study will contribute significantly to advancing the field and improving the industry’s ability to manage and mitigate the risks associated with circumferential crack-like flaws. Completion of this phase represents a significant stride in understanding the load-carrying capacity and model limitations related to C-SCC.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.302
Teacher spread0.269 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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