A 3-D modelling of monopile behaviour under laterally applied loading
Bibliographic record
Abstract
Deploying higher-capacity offshore wind turbines to meet the growing energy demand poses a significant challenge in designing their foundations. Monopiles currently constitute 80% of the foundation installations for these turbines. This study utilizes a nonlinear three-dimensional (3D) finite element model to explore the behavior of monopiles underpinning a five megawatt wind turbine under horizontal loads. The findings reveal that the performance of monopiles is influenced by the strength of the soil and the ratio of pile depth of embedment to diameter (L/D). Examination of flexural bending profiles at/close to failure loads demonstrates the flexible behavior of monopiles, even with a low L/D ratio. The L/D ratio exhibits varying degrees of impact on the normalized ultimate lateral capacity of monopiles, with a notable effect observed in soft clays, resulting in an increase of up to five times for L/D ratios ranging from 3.33 to 13.33. Stiff clays show comparatively lesser effects. Under serviceability loading, an increase in the L/D ratio leads to a 3–6% rise in the maximum flexural moment of monopiles, while the maximum shear force experiences a decrease of 20–30%. Furthermore, a significant reduction, up to five-fold, in the maximum tower tip displacements and rotations at the mudline is observed with an increasing L/D ratio. However, this reduction is more pronounced for higher foundation rigidity.
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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 | 0.001 |
| 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 | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".