Cyclic Lateral Response of Large‐Diameter Monopiles in Soft Clays Using Bounding Surface‐Based Analytical p‐y Curves
Bibliographic record
Abstract
ABSTRACT The p‐y curve method provides a relatively simple and efficient means for analyzing the cyclic response of horizontally loaded piles. This study proposes a p‐y spring element based on a bounding surface p‐y model, which can be readily implemented in Abaqus software using the user‐defined element (UEL) interface. The performance of these p‐y spring elements is validated by simulating field tests of laterally loaded piles documented in the literature. The developed spring element effectively replicates the nonlinear hysteresis, displacement accumulation, and stiffness degradation observed in soft clay. Subsequently, a finite element model of a large‐diameter monopile is established using the proposed spring element. A comprehensive numerical investigation is conducted to explore both the monotonic and cyclic responses of large‐diameter monopiles in soft clays. The results are presented and discussed in terms of pile head load–displacement curves, the evolution of rotation angles at the mud surface, and cyclic p‐y curves. Additionally, empirical formulas are proposed to predict the evolution of cumulative rotation angles and peak bending moments under both one‐way and two‐way cyclic loading conditions. The results provide valuable insights into the mechanism of pile–soil interaction under lateral cyclic loading.
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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 | 0.000 |
| 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".