Prediction of the fracture life of a wrinkled steel pipe subject to low cycle fatigue load
Bibliographic record
Abstract
The economy of Canada depends largely on the performance of the hydrocarbon-based energy industry (oil and gas), which in turn is dependent on the performance of steel pipelines that are used for transporting crude oil, natural gas, and petroleum products. Field observations of buried pipelines indicate that it is not uncommon for geotechnical movements to impose large displacements on the pipelines, resulting in localized curvature, deformations, and strain in the pipe wall. Often these local deformations result in local buckling (wrinkling) in the pipe wall, and in the post-buckling range of response such wrinkles develop rapidly. Subsequent cyclic load histories may produce cyclic plastic strain reversals leading to the formation of fractures in the wrinkle region. This paper presents the development and application of a simple fracture-life assessment model that can be used successfully by the pipeline industries to assess the remaining life before fracture of wrinkled pipelines subject to strain reversals due to low cycle fatigue loadings.Key words: wrinkled pipeline, low cycle fatigue load, plastic strain reversal, fracture, hysteresis loop energy, fracture-life assessment model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".