A bounding-surface-based cyclic “<i>p–y+M–θ</i>” model for unified description of laterally loaded piles with different failure modes in clay
Bibliographic record
Abstract
The increasing turbine sizes have necessitated monopiles in soft clay to have larger diameter and rigidity, from early design of flexible piles to recent semi-rigid piles, with future anticipating rigid piles. Existing few cyclic soil–pile interaction models are developed for flexible pile associated with full-flow failure (above the rotation point, RP), with little attention paid to semi-rigid and rigid piles involving rotational-shear failure (below RP). This study aims to unify the description of piles with varying rigidity by proposing a cyclic two-spring model, where lateral resistances above and below RP are described with cyclic p–y and M– θ springs, respectively. It naturally recovers to a cyclic p–y model for flexible piles. The cyclic p–y and M– θ formulations are developed within the bounding-surface plasticity framework, based on numerical results of cyclic soil–pile interaction concerning full-flow and rotational-shear mechanisms, respectively. These numerical analyses are performed using a cyclic plasticity clay model developed and implemented numerically in this study. The cyclic “ p–y+M– θ” model quantitatively reproduces experimental results of cyclic shakedown and ratcheting for flexible, semi-rigid, and rigid piles. Ignorance of the M–θ spring could underestimate cyclic resistance of rigid piles by 25%, suggesting the model’s merit in reducing conservatism for monopiles in feature designs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".