Optimizing the Unconnected Piled Raft Foundation for Soft Clay Soils: Numerical Study
Bibliographic record
Abstract
Over the past two decades, a number of researchers studied different aspects of the unconnected piled raft foundation (UPRF) system. In this system, a structural fill cushion is inserted between the raft and the concrete piles (PC) where the cushion transfers the loads from the superstructure to the piles. They showed that UPRF could increase the load-bearing share of the raft relative to that of the concrete piles, which leads to a favourable economic impact. Stone columns (SC) and deep-mixed columns (DMC) have also gained substantial popularity in the last few decades, and have shown pronounced success in substituting concrete pile foundations in numerous projects. This paper is an attempt to explore the viability of using SC and DMC in the UPRF system. Different column parameters such as material, stiffness, spacing, embedment length, configuration and raft thickness may affect the design considerations of UPRF systems. The current study investigates the effect of such parameters on the performance of the UPRF. In addition, two cushion alternatives were also studied: structural fill and EPS Geofoam. 3D finite element models of a 16-storey building on soft clay were used to compare the behaviour of different types of UPRF foundations embedded in different types of soft clays. A coupled hydraulic and mechanical model using the Modified Cam Clay soil model was used to model the soft soil.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".