Vacuum pressure distribution and pore pressure variation in ground improved by vacuum preloading
Bibliographic record
Abstract
This paper presents the difference between vacuum pressure and pore pressure reduction for vacuum preloading projects. The experimental results show that the pattern of the fluid flow under vacuum pressure can be classified into three categories—a single-phase water flow, an air–water two-phase flow, and a single-phase air flow. The field test results show that the vacuum pressure reaches the highest value at the ground level and the measured gradients of the vacuum pressure in the vertical direction are approximately 11 kPa/m. It is demonstrated that (i) the treatment area of vacuum preloading cannot be sealed and does not need to be airtight, (ii) the air–water mixture is drawn out from the treatment area under vacuum pressure and the groundwater level drops owing to the presence of air in practice, and (iii) there is an air–water two-phase flow in the unsaturated zone during preloading. The study shows that (i) the vacuum pressure is only a part of the pore pressure reduction along the depth of improving soil; and (ii) the vacuum pressure induces the soil to undergo isotropic consolidation, whereas the pore pressure reduction that is greater than the atmospheric pressure induces the soil to undergo one-dimensional consolidation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".