Influence of gravel density in the behaviour of soft soils improved with stone columns
Bibliographic record
Abstract
Stone columns are frequently employed to improve the bearing capacity of soft soils, to reduce settlements, and to increase the speed of consolidation. Their behaviour depends on several factors, such as the density of the aggregate that forms the column and the area replacement ratio. This paper presents a study of the influence of the density of the gravel forming the columns on the deformation and stresses around end-bearing stone columns installed in soft soils. For this purpose, the behaviour of a horizontal slice of a unit cell has been analyzed by small-scale laboratory tests performed in a Rowe–Barden cell. Tests have been performed with a gravel relative density of Dr = 30% and with two area replacement ratios. Their results have been analyzed along with those from similar tests performed with a gravel density of Dr = 100%. The study is focused on the soil–column stress concentration ratio and the reduction of settlements. Finally, the experimental results are compared with numerical simulations. The results show that a reduction of settlements around 10% occurs when the relative density of the gravel increases from Dr = 30% to 100%. Numerical analyses reproduce well the behaviour of stone columns and are in good agreement with the 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".