Improved Folias Factor and Burst Pressure Models for Corroded Pipelines
Bibliographic record
Abstract
Burst pressure models are used for the fitness-for-purpose assessment of energy pipelines. Existing burst pressure models for corroded pipelines are unable to predict the pipe capacity correctly. In this paper, an improved burst pressure model is developed for corroded pipelines considering the burst pressure of flawless pipes and a reduction factor due to corrosion separately. The equation for the burst pressure of flawless pipe is revised based on the theory of the thick wall cylinder. A new model for the Folias factor is proposed for calculating the reduction factor. The new model for the Folias factor incorporates the depth of corrosion defect, whereas the existing models do not account for the effect of the defect depth. The authors' earlier work revealed that the Folias factor depends on the depth of defect. The proposed burst model reasonably predicts the burst pressures obtained from finite element (FE) analysis conducted in this study and the burst test results available in the published literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".