Predicting the Failure Pressure of SCC Flaws in Gas Transmission Pipelines
Bibliographic record
Abstract
An important requirement for the management of stress-corrosion cracking (SCC) in natural gas transmission pipelines is the ability to predict accurately the burst failure pressure of flaws that have been discovered, particularly those found by crack detection in-line inspection (ILI). ASME B31.8S contains guidance for categorization of SCC based on predicted failure pressure for the cracks. Assessment of the segments is based on the severity category of SCC. As part of a Joint Industry Project (JIP) addressing the management of SCC in gas transmission pipelines, eight operators have assembled information relating to 85 in-service failures, hundreds of hydrostatic test failures, and dozens of pipe burst tests in which failure was due to SCC. Within the database are a wide range of pipe grades and sizes. Failures are due to both high pH and near-neutral pH SCC, and the flaws that initiated failure range from simple thumbnails to complex groups of cracks in a three-dimensional cluster. This paper presents some of the results from a comprehensive comparative study of the failure pressure predictions obtained using API 579 Level II, ln-secant, CorLAS® and PAFFC methods for around 40 of the best-characterized datasets within the above database. From the results obtained, the sensitivities of the calculations to the calculation method used and to the input data, such as flaw profile, are examined. The results provide useful guidance to all those involved in predicting failure pressures as part of their threat management activities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".