Experimental Testing and Evaluation of Crack Defects in Line Pipe
Bibliographic record
Abstract
Pipelines are in wide use throughout the world, and aging pipelines may experience defects such as environmental or stress-induced cracking. The evaluation of crack defects is important for continued safe operation of pipelines. At present, there are several assessment methods for crack-like defects in pipelines including API579, BS 7910, NG18, software applications, as well as numerical modeling approaches. All have been used successfully to evaluate crack defects, but the degree of conservatism and sensitivity to the various input parameters is not known. To address this need, a series of full-scale burst tests was undertaken on end-capped, seam-welded pipe specimens. The tests were carried out on 508 mm (20 inch) diameter Grade API 5L X60 line pipe with a 5.7 mm wall thickness. Elliptical cracks were created by first cutting a longitudinally oriented narrow slit in each pipe and then pre-fatiguing the pipes to create sharp cracks of different depths. Rupture tests were conducted by pressurizing the pipes to failure and the failure pressure was evaluated using current assessment methods. Examination of the fracture surface showed that the pipe sections failed by ductile tearing, as expected for the material and crack sizes. It was found that the Level 3 FAD for API 579 (J approach, using the cylinder equations) and CorLAS provided the most accurate prediction in comparison with the other methods i.e. BS7910 and NG-18.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".