Temperature effects on dredged slurry performance under vacuum preloading
Bibliographic record
Abstract
Under appropriate temperature conditions, vacuum preloading can effectively accelerate the rate of soil consolidation. If the temperature is low (30 °C), vacuum preloading is less effective at consolidating the soil. If temperature is extremely high, vacuum preloading is less efficient at consolidating the soil due to the consummation of excess energy consumed. In this study, a series of laboratory tests was conducted to analyse the effects that temperature has on dredged slurry consolidation via vacuum pressure using constant and variable heating modes. During these tests, heat transfer, water discharge, surface settlement, and pore-water pressure dissipation were observed in the soil samples. Based on the laboratory test measurements, each soil sample’s horizontal coefficient of consolidation, water content, and shear strength were determined. To quantify the energy consumption of the different heating modes, the ratios of energy consumption as a function of the soil’s total water discharge and mean shear strength were determined. Using these parameters, an optimal soil consolidation temperature was obtained. The results indicated that vacuum preloading was most effective in consolidating the soil under a constant temperature of 75 °C rather than variable temperatures.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".