Fracture of Wrinkled Pipes Subjected to Monotonic Deformation: An Experimental Investigation
Bibliographic record
Abstract
Buried pipelines, used by petrochemical industries in North America for transporting oil and derivatives, are often subjected to large deformations resulting from geo-environmental factors and operating conditions, such as geotechnical movements, thermal strains, and internal fluid pressure. Exceeding the critical deformation limit of these pipes initiates wrinkles, and further increase may result in fracture, thus jeopardizing the safe operation of a field pipeline. A recent field fracture and failed laboratory specimens under monotonic load history address the necessity of conducting a full-scale test program to better understand the complete post-wrinkling behavior and failure modes of wrinkled pipes under similar loading conditions. Six tests with two sizes of pipe (NPS16 and NPS20), which are typical of those used in the field for transmission of hydrocarbons, were tested under monotonic axial and bending deformation. Test results in general had shown that both NPS16 (pipe material grade X60) and NPS20 (pipe material grade X65) steel pipelines generally exhibited a ductile behavior after wrinkling. Eventually, these pipe specimens failed due to excessive cross-sectional deformation. Several incidents of rupture or fracture in the pipe wall were observed at the sharp fold of the wrinkle on the compression side of the deformed pipe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".