Large-deformation finite element analysis of pipe penetration and large-amplitude lateral displacement
Bibliographic record
Abstract
Seabed pipelines must be designed to accommodate thermal expansion — which is commonly achieved through controlled lateral buckling — and to resist damage from submarine slides. In both cases, the pipe moves laterally by a significant distance and the overall pipeline response is strongly influenced by the lateral pipe–soil resistance. Here, the process of pipe penetration and lateral displacement is investigated using a large-deformation finite element method, with a softening rate–dependent soil model being incorporated. The calculated soil flow mechanisms, pipe resistances, and trajectories agree well with plasticity solutions and centrifuge test data. It was found that the lateral resistance is strongly influenced by soil heave during penetration and the berm formed ahead of the pipe during lateral displacement. For “light” pipes, the pipe rises to the soil surface and the soil failure mechanism involves sliding at the base of the berm. In contrast, “heavy” pipes dive downwards and a deep shearing zone is mobilized, expanding with continuing lateral movement. The different responses are reconciled by defining an “effective embedment” that includes the effect of the soil berm or wall ahead of the pipe. The relationship between normalized lateral resistance and effective embedment is well fitted using a power law.
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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 | 0.001 |
| 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".