Composite repair of large size diameter pipe with severe metal loss defects
Bibliographic record
Abstract
A full-scale testing program was conducted to evaluate the use of carbon-epoxy composite repair technology to reinforce severe corrosion defects in large-diameter pipes. The technical elements associated with this program included reinforcing up to 85% deep corrosion defects in 24-inch diameter pipe samples, including integrating design equations, testing conditions, and performance subject to cyclic pressure and burst testing at elevated temperatures. The objective of the test program was to evaluate changes in the composite design thickness considering a range of severe corrosion depths based on guidance provided in ASME PCC-2, as well as the pressure capacity based on the methodology embodied in ASME B31G for the effects of layers. The testing program also evaluated the ASME PCC-2 design guidance subjected to cyclic pressure conditions at 60°C (140°F). The composite technology evaluated in this program has aimed to advance the effectiveness of composite repair while also addressing common severe corrosion defects to guarantee the operational life of a pipeline based on an optimized design configuration. This integration of knowledge based on results derived from this program offers substantial promise in guiding future composite repair procedures, structural reinforcement designs, and material choices, culminating in enhanced structural robustness and dependability of high-pressure transmission pipeline systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".