Numerical Modeling of Ice–Seabed Interaction in Clay by Incorporation of the Strain Rate and Strain-Softening Effects
Bibliographic record
Abstract
Abstract Ice gouging is one of the main threats to the safety of the subsea pipelines buried in Arctic coastal regions. Determining the best pipeline burial depth relies on free-field ice gouging analysis and obtaining the resultant subgouge soil deformations. Therefore, improving the accuracy and efficiency of the free-field ice gouging analysis is a key demand in daily engineering practice. The pressure-induced by ice keel through the ice gouging process causes the seabed soil to undergo large localized plastic deformation, where the classical Lagrangian method confronts mesh instability challenges. Also, the conventional Mohr–Coulomb soil model is not able to account for the strain-rate dependency and strain-softening effects, which are significant in ice gouging event. In this study, free-field ice gouging in clay was simulated using a coupled Eulerian–Lagrangian approach. The strain-rate dependency and strain-softening effects were incorporated by developing a user-defined subroutine and incremental updating of the undrained shear strength in abaqus. The comparison of the model predictions with published numerical and experimental studies showed a significant improvement of accuracy. A comprehensive parametric study was also conducted to investigate the effect of various model parameters on the seabed response to ice gouging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".