Superstructure–foundation interaction in multi-objective pile group optimization considering settlement response
Bibliographic record
Abstract
The full potential of pile optimization has not been realized as the interactions between superstructures and foundations, and the relationships between material usage and foundation performance are rarely investigated. This paper introduces an analysis and optimization approach for pile group and piled raft foundations, which allows coupling of superstructure stiffness with the foundation model, through a condensed matrix representing the flexural characteristics of the superstructure. This coupled approach is implemented within a multi-objective optimization algorithm, capable of providing a series of optimized pile configurations at various amounts of material. The approach is illustrated through two case studies. The first case involves evaluation of the coupled superstructure–foundation analyses against field measurements of a piled raft–supported building in London, UK. The potential benefits of pile optimization are also demonstrated through re-analyses of the foundation by the proposed optimization approach. In the second case, the effects of a soft storey on the superstructure–foundation interactions are investigated. These cases demonstrate the importance of properly considering the superstructure effects, especially when the building consists of stiff components such as concrete shear walls. The proposed approach also allows engineers to make informed decisions on the foundation design, depending on the specific project finances and performance requirements.
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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 | 0.000 |
| 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) | 0.002 | 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".