Macro- and micro-mechanics of granular soil in asymmetric cyclic loadings encountered by offshore wind turbine foundations
Bibliographic record
Abstract
Offshore wind turbine foundations are subject to 10 7 –10 8 cycles of loadings in their designed service life. Recent research shows that under cyclic loading, most soils change their properties. Discrete Element Modelling of cyclic simple shear tests was performed using PFC2D to analyse the micromechanics underlying the cyclic behaviours of soils. The DEM simulation were first compared with previous experimental results. Then asymmetric one-way and two-way cyclic loading pattern attained from real offshore wind farms were considered in the detailed parametric study. The simulation results show that the shear modulus increases rapidly in the initial loading cycles and then the rate of increase diminishes; the rate of increase depends on the strain amplitude, initial relative density and vertical stress. It shows that the change of soil behaviour is strongly related to the variation of coordination number, rotation of principal stress direction and evolution of degree of fabric anisotropy. Loading asymmetry only affects soil behaviours in the first few hundred of cycles. In the long term, the magnitude of (γ max − γ min ) rather than loading asymmetry dominates the soil responses. Cyclic loading history may change the stress–strain behaviour of a soil to an extent dependent on its initial relative density.
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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 | 0.000 |
| 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 | 0.001 |
| 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".