Eulerian finite element simulation of spudcan–pile interaction
Bibliographic record
Abstract
Jack-up rigs are commonly employed to perform offshore oil and gas drilling. During the installation of the spudcan foundation, the large volume of soil displaced may induce severe stresses on the piles supporting the adjacent platform. The increasing numbers of jack-up rigs being deployed at close proximity to piled platforms accentuates the need for research into quantifying the effect of spudcan penetration on adjacent piles. The main difficulty with the numerical simulation of spudcan–pile interaction lies in the need to model continuous spudcan penetration in a three-dimensional (3-D) modeling space. In this paper, the three-dimensional Eulerian numerical technique is adopted to perform a coupled interaction analysis whereby the process of continuous spudcan penetration is simulated and the corresponding pile responses are obtained. The salient features of the proposed numerical approach with particular emphasis on the incorporation of a pile in the numerical model are first described. The validation of the numerical model against centrifuge experimental data under different geometric configurations and soil profiles are then presented. The final part of this paper demonstrates the usefulness of the proposed numerical approach through a series of parametric studies. This numerical approach can be readily applied to perform site-specific assessment to mitigate the risks associated with spudcan–pile interaction.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".