Modelling vibrated stone columns in soft clay
Bibliographic record
Abstract
The vibrated stone column technique is an economical and environmentally friendly process that treats weak ground to enable it to withstand low to moderate loading conditions. The performance of the treated ground depends on various parameters such as the strengths of the in-situ and backfill materials, and the spacing, length and diameter of the columns. In practice, vibrated stone columns are frequently used for settlement control. Studies have shown that columns can fail by bulging, bending, punching or shearing. These failure mechanisms are examined in this paper. The study involved a series of laboratory model tests on a consolidated clay bed. The tests were carried out using two different materials: (a) transparent material with ‘clay like’ properties, and (b) speswhite kaolin. The tests on the transparent material have, probably for the first time, permitted visual examination of deforming granular columns during loading. They have shown that bulging was significant in long columns, whereas punching was prominent in shorter columns. The presence of the columns also greatly improved the load-carrying capacity of the soft clay bed. However, columns longer than about six times their diameter did not lead to further increases in the load-carrying capacity. This suggests that there is an optimum column length for a given arrangement of stone columns beneath a rigid footing.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".