Analytical solution for radial consolidation considering soil structure characteristics
Bibliographic record
Abstract
A system of surcharge load combined with vertical drains to speed up consolidation of soft soil by reducing the drainage path is one of the most efficient and economical ground improvement techniques. In the field, conventional theories including smear zone have been commonly employed to predict the radial consolidation behaviour induced by vertical drains in soft clay. One of the key parameters in conventional analysis is the use of mean coefficient of volume compressibility and soil permeability, which are often assumed to be constant. The effect of drain installation on the soil compressibility of the in situ clay structure is often ignored. Laboratory testing has shown that the soil compressibility and permeability can vary nonlinearly over a considerable range of applied surcharge pressure, and both these properties can be affected during the drain installation. This study presents a mathematical model of radial consolidation via vertical drains incorporating the variations of soil compressibility and permeability as well as highlighting the effects of drain installation on those parameters. The main differences between the proposed and conventional models are elucidated, in terms of stress history and preloading (surcharge) pressure. The effects of preconsolidation pressure and the magnitude of applied preloading are examined through the dissipation of average excess pore pressure and associated settlement. Supported by experimental observations, the proposed theory is validated with field data of a selected case study in the town of Ballina, New South Wales, Australia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".