An Improved Experimental Test Set-up to Study the Performance of Granular Columns
Bibliographic record
Abstract
Abstract This paper describes an innovative design of a newly developed large test setup for testing the performance of footings supported on soft clay reinforced with granular columns. This advanced testing method is used to examine the settlement of footings supported on granular columns. Two important features of the equipment are (a) the axial loading system which allows samples to be consolidated under Ko condition while the load is applied onto a small foundation area of the sample, and (b) a relatively large sample size of 300-mm diameter and 400-mm high. The system is also equipped with pressure cells located beneath the footing and top cap to measure the pressure distribution with respect to foundation displacement and a lateral strain gage to monitor boundary effects. This paper reports on some of the early findings from the preliminary tests carried out using this equipment. Samples for testing were prepared by consolidating kaolin slurry in a large one-dimensional consolidation chamber. The granular columns were installed using the replacement method by compacting crushed basalt (uniformly graded with 90 % between 1.5–2-mm particle sizes) into a preformed hole. The preliminary tests have yielded promising results, validating the functionality of the equipment and support the prospect of increasing the knowledge with respect to settlement response and design of a footing supported on granular columns.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".