Centrifugal and numerical modeling of stiffened deep mixed column-supported embankment with slab over soft clay
Bibliographic record
Abstract
A stiffened deep mixed (SDM) column can significantly increase the bearing capacity, reduce settlement, and enhance the slope stability of soft clays as compared with a conventional deep mixed (DM) column. This technique involves inserting plain concrete, reinforced concrete or a steel pile into the center of the DM column after the DM column is constructed. In this paper, a series of centrifugal modeling tests were conducted to investigate the performance of an SDM column-supported embankment over soft clay. A model embankment supported only by DM columns was constructed for comparison. Two ideal numerical models of column-reinforced soil under equal stress and equal strain conditions were established to explore the role the column played in accelerating soil consolidation. A parametric study was conducted to investigate the influence factors of the length of the core pile, column spacing, thickness of the underlying soil, modulus and thickness of the cushion, and modulus of the slab on the load transfer of the system, and some recommendations were proposed for its application. The load-transfer mechanism of an SDM column-supported embankment system with a slab was established based on the development of the volumetric strains and the principal stresses in the numerical models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".