Field Methods for Estimating Pipeline Stress Corrosion Crack Growth Rate at High pH
Bibliographic record
Abstract
Abstract Crack growth rate (CGR) is a critical parameter in pipeline integrity management for estimating the time needed for the next in-line inspection (ILI) or pressure test or the reassessment interval for direct assessment. The current industrial practice of estimating CGRs uses a constant rate or a rate obtained through linear extrapolation. Without including the underlying cracking mechanisms to account for the physical non-linear growth of a crack, such an estimate can be fraught with uncertainties. Mechanistic CGR models for pipeline high pH stress corrosion cracking (SCC) are often sophisticated, contain many model parameters, of which some may not be known or cannot be measured in the field. Thus, direct field use of these models is challenging. This paper reports a method developed for making field use of the mechanistic models by grouping the variables. Such a model with fewer model parameters still retains the mechanistic nature of the original models and, if calibrated with field data, will allow for predicting a future CGR. A four-step procedure was proposed for field use of the models as an alternative to existing methods. A proper CGR should be determined by evaluating all CGRs obtained from different methods and by using expert’s best judgment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".