Slip Line Field Solutions As an Approach to Understand Ice Subgouge Deformation Patterns
Bibliographic record
Abstract
In the Arctic where drifting ice contacts the sea floor, oil and gas pipes need to be buried at a certain depth beneath the sea floor to avoid damage caused by ice scour and the soil plastic deformation induced by ice scour. For a safe design, knowledge is required about the depth and magnitude of the soil deformation, referred to as the subgouge. This paper presents the centrifuge testing results of subgouge deformation and examines the viability of using slip line field solutions as a theoretical approach to estimate the subgouge deformation induced by ice scouring. For centrifuge testing, variables included ice keel speed, undrained shear strength of soil and ice keel scour depth. The soil deformations were determined by image processing of video camera data; and the horizontal and vertical loads were measured using load cells. For the theoretical approach, Petryk’s (1987) slip line field solutions for a rigid wedge sliding over a flat surface of ductile material are used as an analogy to the processes occurring in ice scouring, where ice is the rigid hard material and soil is the ductile material. Investigation shows the deformation mechanism in Petryk’s slip line field solutions resemble what was observed during model tests of ice scouring in clay. The results from the slip line solutions are compared with the testing data. Discussions on the validity of the theoretical approach and empirical equations are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".