Effect of backfilling stiffness and configuration on seabed failure mechanisms and pipeline response to ice gouging
Bibliographic record
Abstract
Ice gouging is a significant issue for offshore structures in cold environments. Pipelines in Arctic regions are buried in the seabed to prevent the direct contact of pipelines and the impacts of soil displacement from ice gouging. However, choosing the appropriate backfilling material and stiffness to maintain the pipeline's integrity while minimizing construction costs is a complex design consideration. It is crucial to accurately model the interaction between the ice, backfill, trench wall, and pipeline to assess the backfill functionality in a coupled ice gouging analysis. This study comprehensively investigated the effect of backfilling stiffness and configuration on seabed failure mechanisms and pipeline response during ice gouging events on a deeply buried pipeline. The study focused on six different backfill materials, including dense and loose sands and very soft clay to stiff clay. The Coupled Eulerian-Lagrangian (CEL) method was used to simulate the large seabed deformation due to the ice gouging process in a trenched/backfilled seabed in Abaqus/Explicit. Incorporation of the strain-rate dependency and strain-softening effects involved the development of a user-defined subroutine and incremental update of the undrained shear strength within the Abaqus software. Key findings reveal that both overly soft and excessively stiff backfill materials can negatively impact pipeline response during ice gouging. Very soft clay exhibits a distinct ''removal'' mechanism, leading to increased pipeline displacement, while overly stiff clay and dense sands result in more significant displacement due to efficient force transfer. The results can inform the selection of appropriate backfill materials and backfilling techniques to enhance pipeline protection against ice gouging.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".