Development of unified <i>p–y</i> curve model for clays using finite element analysis of laterally loaded piles
Bibliographic record
Abstract
In this study, a unified p–y curve model is developed for clays in undrained conditions using the results of 3D finite element (FE) modeling. The p–y curves are primarily used in the analysis of laterally loaded piles. Formulas for the ultimate lateral bearing capacity factor ( N p ), and the reference deflection in the p–y curve ( y 50 ), were obtained using the results of parametric studies and regression analysis. The tangent hyperbolic function was used to model the p–y curve shape. In the FE models, the clay soil material was modeled as elastic-perfectly plastic material using Mohr–Coulomb criteria, and the pile material was modeled as elastic only. The parametric study results show that N p varies nonlinearly with depth. The proposed model for N p is composed of two regions: a nonlinear zone and a linear zone. The model for N p used the undrained shear strength ( s u ), effective unit weight of soil ( γ′), and pile width ( D) as parameters. Further, the proposed model for y 50 was found to be dependent on the soil stiffness ( E s ), D, and N p . The dependency of y 50 on N p was often overlooked in previous studies. Finally, the proposed model was imported in LPILE program and the results from previous case studies were compared with the proposed model predictions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".