An energy-saving loading strategy: cyclic vacuum preloading treatment of soft ground
Bibliographic record
Abstract
Vacuum preloading is a commonly adopted method for improving soft ground. During the preloading period, the applied vacuum pressure is constant or multistaged, implying that the vacuum pump must work continuously with constant or increasing operating power. To reduce the energy consumption of the vacuum pump, a new loading strategy of cyclic vacuum loading was proposed in this study, whereby the pump is periodically switched on and off, and hence the vacuum pressure ascends and descends accordingly. To guide the design of cyclic vacuum preloading, a large-strain radial consolidation theory incorporating the shifting of the consolidation status of ground soil from normally consolidated to overconsolidated (and vice versa) was established in the present study. Based on the established theory, the treatment effects of cyclic vacuum loading can be quantitatively determined, including the ground settlement and the degree of consolidation, while the energy consumption can be simply determined by multiplying the power by the operating time of the vacuum pump, which works only during the ascending stage of cyclic vacuum pressure. Two cases of the cyclic vacuum preloading strategy were evaluated herein, that is, the complete cyclic vacuum loading and combined cyclic/constant vacuum loading cases. Finally, experimental tests were conducted to demonstrate the energy-saving effect of cyclic vacuum preloading. Compared to the conventional vacuum preloading strategy (i.e., constant vacuum pressure), the energy consumption of the cyclic vacuum loading strategy can be drastically reduced (80% reduction), while the treatment effects remain basically unaffected.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".