Prediction of Environmentally Assisted Cracking on Gas and Liquid Pipelines
Bibliographic record
Abstract
A model has been developed to predict crack growth on pipelines from environmentally assisted cracking in near-neutral pH environments (often-termed low-pH stress corrosion cracking (SCC)). The model is based on the results of cyclic loading experiments and is used in conjunction with pressure time variations in the pipeline determined from the operating SCADA records to predict the growth of an assumed existing crack in the pipe. The crack grows through different crack growth regimes, which are determined by the size of the pressure variations and the instantaneous crack dimensions. For a growing crack that experiences relatively high pressure fluctuations, as often encountered on liquid lines, reasonable crack growth predictions were made based on corrosion fatigue. An approach based on crack tip strain rate appears more suitable for the prediction of crack growth of small cracks and for cracks on gas lines with small pressure fluctuations. The model is designed so that the effect of stress intensifiers (like the long seam weld crown) that are often associated with these failures can be included. The model can be used in its present format for prioritizing inspections on both gas and liquid pipelines. Whereas predicted crack growth rates compare favorably with rates measured in the field, further work is required to incorporate additional mechanical and environmental effects, in particular to improve the prediction of small crack growth rates. Low crack velocities may be possible in the presence of small pressure fluctuations and low frequencies, but they may be less probable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".