Assessment of Crack-Like Flaws in Pipelines
Bibliographic record
Abstract
Abstract Inspection of pipelines may reveal crack-like anomalies. United States standards require that crack-like features by repaired or removed from pipelines. In contrast, Canadian standards permit an engineering critical assessment (ECA) of crack-like features. ECA utilizes pipeline dimensions, operating pressures, material properties, fracture mechanics, and inspection data to determine the disposition of crack-like anomalies. Methods for performing an ECA are reviewed. They include estimation of failure conditions for toughness-controlled fracture and the potential of crack growth by fatigue, stress-corrosion cracking, or corrosion fatigue. Application of the failure assessment diagram (FAD) as well as inelastic fracture mechanics is discussed. The importance of pressure cycle counting is pointed out. The rain flow cycle counting method is extended to incorporate cyclic frequency so t¡me/cycle- dependent crack growth can be evaluated. Practical examples are presented to illustrate the application of ECA.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".