Composite Sleeve Repair in the North American Regulatory Environment
Bibliographic record
Abstract
Abstract Composite sleeve repairs have been used in the pipeline industry for the last 25+ years. Fiberglass sleeves (e.g., Clock Spring®) were initially introduced in the market and are still being used as a proven pipeline repair method. For the last 15+ years, new composite materials have been introduced in the industry to provide a wider variety of repair options depending on the type of imperfections being repaired. Regulations in the U.S.A. and Canada share some requirements regarding design, installation, testing, and assessment of composite sleeve repairs. The U.S. Department of Transportation (DOT) through the Pipeline and Hazardous Materials Safety Administration (PHMSA) recommends the use of repair methods consistent with industry standards. The 2019 version of the Canadian CSA Z662 Oil and Gas Pipeline Standard includes requirements for testing and qualification according to the American Society of Mechanical Engineers (ASME) regulation PCC-2 or ISO/TS 24817, and requirements for conducting an engineering assessment to determine the subsequent maximum stress on the pipe sleeve. This paper compares the regulatory requirements for pipeline composite sleeve repairs in the U.S.A. and Canada; it describes some of the options for composite sleeve repair, and reviews engineering assessments of methodologies for composite sleeve repair.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".