Lateral resistance of “rigid” pipelines and cables on rocky seabeds
Bibliographic record
Abstract
Accurate assessment of lateral resistance is critical to ensure the on-bottom stability and integrity of subsea pipelines and cables in the oil–gas and marine renewable energy industries. However, on rocky seabeds recommended practices provide limited recommendations on pipe–seabed interaction, suggesting only a single value for the friction coefficient of 0.6. This paper reports on a programme of physical experiments and theoretical modelling investigating the lateral resistance of pipes on rocky seabeds. It is shown that the peak and mean effective friction can significantly exceed the interface (or Coulomb) friction coefficient when the pipe diameter (D) is similar to the median rock diameter (dn50). Only when the pipe diameter becomes large compared to the rock size does the mean effective friction approach the interface friction. The effective friction coefficient was found to vary with variability in rock size and shape, as well as the length of pipe relative to median rock diameter. Each of these findings is reproduced well using the theoretical model. Collectively, the results demonstrate that the effective lateral friction coefficient may be higher than 0.6 for mean friction, and significantly higher for peak friction. This implies that inaccuracy may exist in current design, which may be rectified using the theoretical model.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".