On the Effectiveness of Composites for Repair of Pipelines Under Various Combined Loading Conditions: A Computational Approach Using the Cohesive Zone Method
Bibliographic record
Abstract
ASTM PCC-2 standard provides a series of equations for establishing the composite repair's thickness required for bringing the capacity of dented/damaged pipes, to their original design state. However, the accuracy of the equations' predictions for pipes subjected to various combined loadings has not been fully explored. Moreover, the influence of the state of a pipe/composite wrap (CW) interface (i.e., whether perfectly intact or not intact), in reference to the predictions of the ASTM equations, has not been studied either. In consideration of the above-mentioned issues, a comprehensive finite-element (FE) study is conducted, using the cohesive zone methodology (CZM) to simulate the response of pipes repaired with composite wraps, under single and various combined loading conditions. Moreover, the influence of perfect (or tied) and imperfect (unintact) pipe/CW interface on the load-bearing capacity of repaired pipes is systematically investigated. Finally, the effects of composite repairs' thickness and length on their efficacy are also investigated. The results show that, although the pipe/CW interface state does not have any noticeable effect when the pipe is subjected to a combined loading state of bending moment and internal pressure, it plays a crucial role when the pipe is under a combined internal pressure and uniaxial loading condition. Furthermore, the predicted values calculated according to the ASME standard are compared with the finite-element results, demonstrating that ASTM-based predictions do not provide accurate results when a repaired pipe is subjected to an axial loading condition.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".