Physical model case study: treatment effect of soft ground by vacuum preloading combined with liquid bag pressurization method
Bibliographic record
Abstract
Vacuum preloading and composite ground reinforcement are commonly used methods for reinforcing soft soil, but there is a lack of integrated design method for vacuum preloading combined with composite ground. This case study introduces an innovative approach that combines vacuum preloading with liquid bag pressurization to achieve the integrated design of consolidation drainage method and composite ground reinforcement, which is different from the reported air bag pressurization. To illustrate the effectiveness of this method. Model tests were carried out to analyze the variation of water discharge, pore water pressure, ground settlement, and average consolidation degree in the process of vacuum consolidation. The study investigated the water content, undrained shear strength, and ground bearing capacity of composite ground after ground treatment. A correlation between average undrained shear strength and characteristic value of ground-bearing capacity was established to evaluate and predict the treatment effect of composite ground. Research shows that compared with traditional vacuum preloading, the undrained shear strength can be increased by 13.78%–65.08%, and the characteristic value of bearing capacity for the composite ground can be enlarged by 2.3–4 times. These results indicate that the vacuum preloading combined with liquid bag pressurization can significantly improve reinforcement effect on soft ground.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".