3-Dimensional Numerical Evaluation of Geosynthetic Encased Stone Columns in Unsaturated Soils
Bibliographic record
Abstract
Geosynthetic encased stone columns are often designed using the conventional framework of saturated soils ignoring the influence of in-situ unsaturated soil conditions. This article evaluates the performance of stone columns with and without geosynthetic encasement extending the mechanics ofunsaturated soils. The focus of numerical simulations was directed towards understanding the influence of matric suction on the confining support offered by the surrounding soil to stone columns with and without geosynthetic encasement. Investigations were extended considering the stiffness and the length of the geosynthetic encasement. The numerical studies suggest the load-carrying capacity of stone columnincreased with an increase in the matric suction in the boundary effect and the primary transition zone. However, the contribution of matric suction towards load-carrying capacity starts reducing from the secondary transition zone. The information on boundary effect and transition zones can be derived fromthe soil-water characteristic curve, which is a relationship between the water content and soil suction. In addition, the effect of stiffness and length of encasing material in unsaturated soils was found to be in contrast with saturated soils. The results of the study are promising towards developing procedures thatcan be used in the rational design of stone columns in unsaturated soils.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".