New Application for Fiber Wrap Strengthening of Buried Pipelines
Bibliographic record
Abstract
Since its first uses and patents in the mid-nineties, the application of carbon fiber reinforced polymer (CFRP) strengthening (fiber wrap) in pipelines has grown to become a widely used and recognized repair and strengthening system for aging prestressed concrete cylinder pipes (PCCPs) with broken prestressing strands. The ability of FRP repair techniques to allow the host PCCP sections to resist high internal pressures and as well as external loadings without requiring excavation of the pipe provide advantages for the fiber wrap technology over existing techniques. Advantages of FRP repair of PCCP is specially highlighted when considering the time and cost savings provided by a trenchless approach in congested areas or the political cost of open cut repair methods. Improvements in materials, design, workmanship and safety are now opening the door for new applications beyond the traditional fiber wrap strengthening application of PCCP lines which have broken prestressing strands. Applications of FRP strengthening to sewers, tie-offs between old and new pipelines, strengthening of manhole section (existing or to come) and uses on other pipeline materials such as RCP or steel are becoming more and more common. This paper will discuss a new application for fiber wrap strengthening of pipelines in which a new steel pipe, used as part of an emergency repair, was strengthened using FRP in order to achieve the desired capacity.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".