Prediction of Corrosion Defect Failure Pressure for Finite Length Defects
Bibliographic record
Abstract
Corrosion defects commonly occur on operating pipelines due to a loss of protection in a corrosive environment. These defects require practical and accurate assessment, particularly for older pipeline systems, to determine the need for remediation or allow for continued operation. Previous research has shown that appropriate application of full three-dimensional finite element analysis, and newly developed analytical approaches, can provide very accurate predictions of failure pressure but require detailed material and geometric data. Although this is important, a simpler method that allows for efficient evaluation of large amounts of data is also desirable. A method has been developed from an existing analytical solution by assuming a defect can be characterized in terms of the total defect length, and a constant defect depth equal to the maximum defect depth. In general this produces a conservative estimate of the material loss. This finite-length defect solution is in good agreement with experimental data for idealized defects, and provides reasonable predictions of burst pressure, with a minimum amount of data, when applied to real corrosion defects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".