A Limit State Function for Pipelines Containing Long Corrosion Defects
Bibliographic record
Abstract
Currently, there exist various models that predict the burst capacity of a pipeline containing corrosion defects. Recent studies have indicated that these models tend to be overly conservative for long corrosion defects. This paper, based on a PRCI-sponsored study, aims at minimizing this conservatism through a series of steps. First, different definitions for long corrosion defects prevalent in the literature were examined and compared, and the most suitable criterion was implemented. Next, three existing burst pressure models for general corrosion defects were identified and evaluated: ASME B31G-modified, a model developed at C-FER and a model developed at the University of Waterloo. The suitability of these models for long corrosion defects was assessed using a database of 50 full-scale burst test specimens containing natural long corrosion defects. Finally, based on this evaluation, the most apposite burst pressure prediction model for long corrosion defects was selected and a corresponding model error factor was derived.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".