Assessment of the Environmental Effects on the Performance of FRP Repaired Steel Pipes Subjected to Internal Pressure
Bibliographic record
Abstract
The use of composite materials for repair and rehabilitation of corroded steel pipes has been increasingly growing in the oil and gas industry. However, there exists a noticeable gap in the literature on the long term performance of composite repaired pipes, especially those subjected to large internal pressure magnitudes. This work is an attempt toward filing the gap by gaining a better understanding of the effects of environmental conditions on the long term performance of composite repaired pipes subjected to large internal pressures. Finite element method (FEM) is used to simulate typical composite warp-repaired gouged steel pipes, conditioned in various environments and subsequently subjected to internal pressure. The influence of the resulting degradation in composite’s mechanical properties on the performance of the system was evaluated. To validate the results, an experimental program was designed and carried out. Repaired specimens were conditioned in an environmental chamber under certain thermal and moisture conditions; then, the specimens were tested to failure subject to internal pressure. Good correlation was obtained after fine tuning of FEM model’s material data through the use of the experimentally obtained data.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".