Impact of Model Errors of Burst Capacity Models on the Reliability Evaluation of Corroding Pipelines
Bibliographic record
Abstract
This paper quantifies the impact of model error associated with the burst capacity model on the probability of burst of corroding oil and gas pipelines due to the internal pressure. Three burst pressure models that are widely used in the pipeline industry, namely the B31G Modified, det norske veritas (DNV), and pipeline corrosion failure criterion (PCORRC) models, are considered in the analyses. The time-dependent probabilities of burst of three hypothetical examples, which are representative of the oil and gas transmission pipelines in the United States, are evaluated by using the first-order reliability method (FORM) to carry out the comparative study. The analysis results indicate that the model error has a substantial effect on the burst probability evaluated. The probabilities of burst evaluated by considering the model error can be several orders of magnitude higher than those evaluated by ignoring the model error. The results underscore the critical importance of including the model error associated with the burst capacity model in the reliability analysis of corroding pipelines.
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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 | 0.004 | 0.021 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".