Pipeline Crack Half-Life Versus 1.10 Safety Factor at Next Inspection
Bibliographic record
Abstract
Abstract Periodic inspection is a proven approach to structural integrity management of transportation systems. This is as true for pipelines as it is for aircraft and railways. Setting the re-inspection interval to ensure imperfections cannot grow to critical dimensions prior to the next inspection is a foundational requirement of this maintenance methodology. API 1176 delineates two methods for re-inspection interval criteria for pipeline crack threat management: (1) maintaining a safety factor of 1.10 and at least a 30% of wall thickness remaining ligament depth until the next inspection, or (2) inspect at the half-life of the feature with the lowest remaining life taking the end of life being a 1.00 safety factor. Recent proposed regulatory documents and draft rules have down-selected to Method (1), or at least demonstrating compliance to Method (1), which will require some operators who have only been using Method (2) to safely manage this change. The two methods are compared for every asset of a large North American operator under current actual operating conditions. The relative conservatism of the two methods is directly compared. Sensitivity to a minimum remaining ligament requirement less than the recommended 30% of wall thickness is explored, and leak/rupture threat differentiation is considered. Implications of the change for a liquids pipeline operator in North America are described.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".