Bibliographic record
Abstract
Icebergs and ice ridges frequently scour the surface of seabed deposits. Ice scouring can be problematic for offshore pipelines and other seabed installations. In order to reduce the risk of failure, pipelines are buried in the seabed. However, a stationary or moving ice feature could cause a significant increase in stresses and deformation in the seabed soil deposits below the contact surface between the soil and the ice, and consequently, might result in structural failure of buried pipeline. Safe burial depth for pipelines has been the subject of both experimental and numerical studies. In this paper, two and three dimensional analyses are conducted using PLAXIS and ADINA. In these analyses, geometric and material nonlinearities are considered. In order to establish the validity of the finite element calculations, the experimental results reported in the literature and the numerical results obtained in the present study are compared. The emphasis is placed on determining the importance of (1) using interface elements between different materials such as soil and ice, soil and pipelines; (2) using the soil model correctly, and (3) using a three dimensional analysis rather than a two dimensional analysis. The changes taking placed in the deformation pattern and the stress states in the seabed soils resulting from ice scouring are determined. The effects of pipeline burial depth, the shape of the ice feature, and the characteristics of seabed soils on the stresses acting on the pipeline are evaluated.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".