Revised Corrosion Management With Reliability Based Excavation Criteria
Bibliographic record
Abstract
For decades, TransCanada Pipelines has used inline inspection (ILI) to manage the threat of corrosion on gas pipelines. Immediate integrity was addressed using rupture pressure ratio and leak criteria, and future integrity was addressed using a growth assessment. However, a review of excavations based on predictions of defect growth shows that few excavations actually lead to repairs. This study investigated the areas of undue conservatism in both the integrity assessments and the excavation criteria. All aspects of the immediate and future integrity assessment based on ILI were examined. All relevant uncertainties were accounted for in calculating the reliability of the pipeline. A new basis of defining excavation criterion was established based on a reliability assessment and calibration. This criterion was validated against previous ILI based excavations which reveal the features that actually required repair. The criterion was also compared to reliability based criteria recommended in Annex O of the CSA Z662 which gives guidelines for Reliability Based Design and Assessment. This paper addresses the practical limitation of data and presents methods for extracting best information from available data. Case studies that demonstrate the application of the revised assessment method and criterion are also discussed.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".