Bibliographic record
Abstract
Due to the complexity of piled-raft system, and due to lack of rational solution, the design of piled-raft foundations relies on numerical modeling using techniques such as finite element.This study is directed to develop a numerical model capable to analyse the problem stated and to identify the parameters governing their performance.The model developed was based on the finite element technique and accounts for the complex interaction factors such as pile-to-pile, pile-to-raft, raft-to-raft and pile-to-soil.The results produced by the present model were validated by the available data in the literature.The model developed herein was then used to conduct a sensitivity analysis on the governing parameters believed to control such behaviour to include: the pile diameter, pile length, pile spacing, pile modulus of elasticity, reduction factor of the pile-soil interface strength, raft width, raft thickness and raft modulus of elasticity.Furthermore, the effects of soil modulus of elasticity, Poisson's ratio, friction angle, dilatancy angle, unit weight, were also examined.The study focussed on the influence of these parameters on the load-settlement relationship and the load sharing between the raft and piles of the system.This study compares the effect of the above parameters on the load-settlement relationship of piled-raft systems at small and large settlements.The study considers the case of a piled-raft supported by a single pile, and piled-rafts supported by (2×2), (3×3), (4×4) and (5×5) pile groups.The result of this phase was useful in optimizing the design of piled-raft foundations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".