Modeling the Stone Column Behavior in Soft Ground with Special Emphasis on Lateral Deformation
Bibliographic record
Abstract
Among various ground-improvement techniques, soft-soil reinforcement by stone columns is one of the most common and convenient methods with numerous benefits including increased bearing capacity and consolidation, reduced postconstruction settlement and lateral movement, and improved slope stability and liquefaction control, among others. Because of the limited confinement offered by the surrounding soft soil especially at shallow depths, lateral deformation of stone columns is not uncommon. Although several analytical and numerical solutions are available to predict the load-settlement performance and consolidation characteristics of stone-column-improved soft ground, most existing models do not accurately capture the lateral deformation of stone columns. In view of this, the authors have developed in-house a novel numerical model based on the Fast Lagrangian Finite-Difference technique with associated subroutines to analyze the behavior of a stone column including its lateral deformation. In particular, the displacement compatibility and the barreling effect are considered in the model. Soft-soil consolidation under imposed loading is considered by adopting the modified Cam-clay theory. The proposed solutions are validated using available field observations and existing numerical solutions. The model was successfully applied to a selected case study at the Pacific Highway near the town of Ballina, New South Wales, Australia. It is demonstrated that both the deformation pattern and bulging depth of stone columns are dependent on several factors including the particle interlock, imposed load-intensity, soil-column stress concentration ratio, soil shear strength, and column geometry.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".