Load Sharing Mechanism of Micropiled-Raft Foundations in Sand
Bibliographic record
Abstract
Micropiled-Raft (MPR) foundations are economical and easy to install. In this study, the load sharing mechanism of micropiled-raft foundations in sand was examined. A series of experimental tests were performed on small-scale models in sand with different relative densities. The effects \nof micropile spacing and relative density of the sand on the overall load-settlement behavior and the load-sharing mechanism were examined. \n \nThe experimental test results served to validate a series of numerical models, which were employed to produce data for a wide range of governing parameters. The effects of the micropile spacing ratio, the relative density of the sand, and the thickness of the raft were examined. While the raft stiffness only marginally affects the overall load-settlement behavior, yet, the load sharing is impacted. \n \nFinally, the Poulos-Davis-Randolph (PDR) method was evaluated against the data produced by the numerical models. It was concluded that the PDR method was occasionally capable of determining the axial stiffness of the MPR with an acceptable range of error, however in general, it overestimated the axial stiffness of the MPRs. Thus, a modification factor was proposed which was validated by the present experimental results
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".