A Test Method to Determine Low-Cycle-Fatigue Behavior of Wrinkled Pipe
Bibliographic record
Abstract
Abstract Field observations of buried pipelines used by energy industries for transporting natural gas and oil indicate that it is not uncommon for geotechnical movements to impose large displacements on buried pipelines resulting in localized curvature, deformations, and strain in the pipe wall. Often these local deformation results in local buckling in the pipe wall (wrinkling) and, in its post-buckling range of response, wrinkles develop rapidly. Subsequent cyclic load histories may produce cyclic plastic strain reversals in the wrinkle region leading to formation of fractures in the wrinkle region. This paper presents an innovative and simple material test method, called a strip test, which was designed and carried out at the University of Alberta in order to simulate the complicated behavior of pipe wrinkles subject to such low-cycle-fatigue loading. It is found that the strip test is capable of replicating the complicated behavior of wrinkled pipe subject to plastic strain reversals at the wrinkle location due to low-cycle-fatigue loading and provides necessary information that can be used for further studies. For the current project, a total of 16 such strip tests were carried out, and the test data from these strip tests have been used successfully to develop a fracture life assessment (FLA) model for the wrinkled energy pipe subject to strain reversal due to low-cycle-fatigue loading. The development of the FLA model will be presented in a future publication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".