Random Field Analysis of Laterally Loaded Monopile Foundations
Bibliographic record
Abstract
Monopile foundation design for supporting offshore wind turbines has largely relied on classical p-y approaches originally developed for longer and more slender pile foundations. The shorter length and greater diameter of monopoles, however, has required empirical modification to the p-y approach to account for shear deformations and coupling between deflections and rotational deformations. Analysis of laterally loaded monopiles is fundamentally 3D, and recent studies have aimed to calibrate p-y springs using full 3D finite element analyses. This paper presents a preliminary study that uses a pseudo-3D approach suitable for modeling axisymmetric solids subjected to non-axisymmetric loading that is ideally suited for this type of geotechnical system. The method uses 2D finite element analysis in radial planes with circumferential variations modeled using a Fourier series. Soil property variation with depth is modeled using random fields. For risk assessment studies requiring Monte-Carlo simulations, the pseudo-3D approach offers greatly reduced computational costs compared with full 3D analysis. While the pseudo-3D approach is not new, it has not, to the authors’ knowledge, been combined with a random field description of soil properties. The results presented in this preliminary study focus on estimation of the probability of excessive lateral deformations at the submerged ground surface.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".