Contribution to the design methodologies of piled raft foundations under combined loadings
Bibliographic record
Abstract
Although simplified design methods for piled raft foundations have been proposed to allow for the group effect and soil–pile–raft interaction, most of them are concentrated on one type of loading, rendering the applicability of these methods limited to cases under such loads. In the case of a combined pile raft foundation (CPRF), the structural loads are carried partly by the piles and partly by the raft as a function of the foundation settlement, rendering the CPRF a complex soil–structure interaction issue. Despite the recent development of computational resources and advances in numerical expertise, a detailed three-dimensional (3-D) numerical analysis, accounting for soil nonlinearities, nonlinear behavior of the interfaces between the soil, piles, and raft under various combinations of loadings remains impractical. The objective of this paper is to provide a rather simplified and straightforward design methodology for pile foundations under combined loadings. To achieve this goal, previous research works on the group effect under axial and lateral loading have been evaluated and the piles–raft interaction effect has been considered. The proposed procedure is fully compatible with structural software codes and can be straightforwardly applied to the design of the structural members, as it is able to effectively solve a CPRF under the numerous combinations of loadings required by most design codes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".