Literature Review of Repair Technologies for Wrinkled Pipelines
Bibliographic record
Abstract
Abstract Wrinkles on a pipeline, whether produced intentionally by construction methods of vintage pipelines or unintentionally by bending loads from subsurface geotechnical movements, introduce significant stress concentration factors. However, common options for pipeline repair usually cannot be used given the protruding wrinkle geometry (e.g. steel sleeves), or are costly and can introduce additional safety concerns (e.g. pipe replacement). Numerous composite repair technologies have been developed that take the form of the underlying structure and, thus, may provide an alternative for this application. However, composite repairs have focused on restoring axial defects in pipelines (i.e. hoop reinforcement), while restoring the bending capacity of wrinkled pipe is less common. Therefore, this literature review consolidates the current state of knowledge regarding the effects of composite repairs on the bending load capacity of pipes. The reviewed literature identified 14 studies (using finite element analysis, full-scale testing, or a combination of both) that investigated composite repairs on wrinkled pipe or under bending loads. Typically, for pipe with non-sharp flaws (e.g. corrosion or wrinkles), the bending capacity of the pipe with a sufficient repair is increased near or beyond that of pristine pipe. The latter case usually results in a new wrinkle forming outside of the repaired pipe section. Most repairs have also been shown to prevent significant plastic deformation of the base pipe beneath the repair. However, knowledge gaps are also identified by this review and present opportunities for future studies to further improve the performance of composite repairs for this application.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".