An Assessment on the Plastic Capacity of Pipe-in-Pipe Systems Under Damage Progression Effect
Bibliographic record
Abstract
Abstract In recent years, pipe-in-pipe (PiP) systems have been employed in an increasing number of subsea projects. According to the previous studies, the external pressure required to develop the initial local buckle on the PiP system is significantly higher than the pressure required to propagate the buckle along the system. In this respect, it is reasonable to investigate a novel topic where the propagation of buckle is induced by a lateral interference load instead of external pressure (e.g., diagonal fishing gear impact). On this subject, the recent studies showed the progression of plastic damage along a single-walled pipe, which is induced by a lateral load, could significantly lower the load-carrying capacity of the pipe. The present study investigates this finding for a PiP solution under a two-phase loading condition: in phase 1, the PiP solution is subject to 75 mm perpendicular indentation, and in phase 2, the resulting plastic damage in phase 1 is translated and induced longitudinally along the PiP system. Furthermore, using finite element analyses, the effect of combined loading (axial and lateral load) on the load-carrying capacity of the PiP specimen is investigated. The test results show that upon the initiation of damage progression, the load-carrying capacity of the PiP specimen (against the lateral indentation) declines by 10%. Also, the numerical results show that the structural resistance of a PiP specimen against a lateral indentation drops significantly when the inner pipe is subject to axial compression.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".