Crack Shape Development in Fatigue Growth Assessment for Pipelines
Bibliographic record
Abstract
Abstract Assessment of fatigue crack growth for pipelines is critical to evaluate the effects of operational pressures acting on flaws and predict the remaining service life. Under pressure cycling, cracks propagate continuously until they reach a critical size, resulting in a pipeline leak or rupture. When the crack grows, the crack shape and size evolve and it is important to characterize these changes to accurately predict fitness. Currently, it is typical to only consider growth in crack depth when modeling pressure cycling-induced fatigue. In some cases, neglecting the effects of crack shape or aspect ratio under crack growth may result in inaccurate predictions for remaining service life and ultimately a reduced margin of safety. Developing a full understanding of crack shape development during crack growth can be crucial for integrity management to more accurately estimate the remaining service life and prevent pipeline leaks or ruptures. In this work, crack aspect ratio change was studied for pipelines undergoing pressure cycling. The effects of initial crack aspect ratio, pipeline diameter and wall thickness, and loading conditions on the crack shape development were investigated. A new methodology for fatigue crack growth assessment is demonstrated. The study provides a refinement to fatigue crack growth assessment with the potential for more accurate prediction of remaining service life for 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".