Strength of Line Pipe With Dent and Crack Defect
Bibliographic record
Abstract
Pipeline industry and various research organizations have been undertaking studies to understand how the pressure strength of line pipes reduces as the defects in the line pipes grow. Defect in pipe lines can be in the form of corrosion, dent, wrinkle, gouge, crack, and combinations of these. A large number of studies have been completed in developing methods for determining the pressure strength of line pipes with dent and gouge defects and also in the form of combined dent-gouge defect. Some of these studies were undertaken with the intention of determining the pressure strength of line pipes when a combined dent and crack (dent-crack) defect has formed. However, in these studies no cracks were simulated in the test pipe specimens; instead, a gouge (machined cut or notch) was produced and considered as a crack. Therefore, it is not realistic to call this defect a dent-crack defect; rather, it should be called dent-gouge defect. Hence, the current project is being undertaken at the University of Windsor to study how the dent-crack defect influences the pressure strength of line pipes. In this study, a crack in true sense was introduced in the pipe wall. Two different techniques were used to simulate the crack in the pipe wall. This paper discusses the procedures used in this study to simulate crack and dent. In addition, the test procedure and test data obtained from denting and pressure tests are discussed.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".