Numerical modelling of soft soil stabilized by vertical drains, combining surcharge and vacuum preloading for a storage yard
Bibliographic record
Abstract
This paper presents a finite element analysis of a case study of a combined vacuum and surcharge load through prefabricated vertical drains (PVD) at a storage yard at Port of Tianjin, China. The top 15 m of soil at this site was very soft to soft and needed to be improved using preloading surcharges of more than 140 kPa. To avoid any stability problems associated with a high surcharge embankment, 80 kPa vacuum pressure combined with fill surcharge was applied (40 and 58 kPa for sections I and II, respectively). A plane strain analysis was performed using equivalent permeability and transformed unit-cell geometry. The converted (equivalent) parameters were incorporated in the finite element code ABAQUS, using the modified Cam-Clay theory. The performance of a trial embankment at the site of the storage yard is predicted on the basis of a constant vacuum pressure applied on the soil surface and distributed along the length of the drain. The predictions of settlement, pore-water pressure, and lateral displacement were compared with the available field data, and an acceptable agreement was found based on this numerical approach. The combination of vacuum and surcharge load can effectively shorten the preloading period, reduce the height of the embankment, and counterbalance excessive lateral displacements.Key words: consolidation, finite element analysis, plane strain method, soil improvement, vertical drains.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".