Theoretical solutions of laterally loaded fixed-head piles in elastoplastic soil considering pile-head flexural yielding
Bibliographic record
Abstract
Group piles with a cap under lateral loading behave like fixed-head piles because of the rotational restraint of the pile cap. They are susceptible to flexural yielding at the pile head due to high bending strains. In addition to soil nonlinearity, the pile-head flexural nonlinearity also significantly contributes to nonlinear responses of the piles, which is not covered in most existing analytical solutions. For a fixed-head pile buried in uniform elastoplastic soils, this study derives analytical solutions for the lateral responses to account for soil yielding, and flexural yielding and failure of the pile section at the pile head. The model assumes elastoplastic Winkler-type soil with constant subgrade stiffness and yield strength and a nonlinear moment–curvature curve for the pile section. Examples are provided to apply the solutions to determine the complete capacity curves (moment and horizontal load) of a fixed-head pile and the derived analytical capacity curves are in good agreement with those from numerical analyses that use nonlinear beam elements to reflect the nonlinear flexural behavior of the pile section. The solutions are also applied to evaluate the influences of the yield displacement of the soil and different forms of simplified nonlinear moment–curvature curves of the pile section on the lateral load–displacement curves.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".