Predicting the Failure Pressure of SCC Flaws in Gas Transmission Pipelines
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it