Electro-osmotic-assisted consolidating of undisturbed soft sensitive clay
Bibliographic record
Abstract
Electro-osmotic consolidation has been recognized as a potential pathway to quickly drain dredged sediments and highly compressible disturbed clay. However, its efficacy in consolidating sensitive undisturbed soft clays found in the Nordic regions are not present. Even before initiating these techniques in the field, laboratory assessment of the electro-osmotic assisted drainage rate, consolidation parameters of the clay and corrosion potential of the electrodes are essential information for engineers. This laboratory study investigates the effect of voltage (10, 20, and 30 V) on the consolidation parameters of undisturbed soft clays using only electro-osmosis and then a coupled approach (incremental loading with intermittent current). The efficacy of the exit hydraulic gradient (in m/s) increased by three-to-four orders regardless of the voltage used in the case of only electro-osmosis assisted consolidation. The electro-osmosis assisted consolidation allowed for consolidation up to 30% at in situ stresses (12–15 kPa) and within 2 h. Such rapid consolidation contrasts with 47% consolidation achieved when preloading is applied even at 1000 kPa, which in field would take place in months. When the coupled approach was utilized, even at 50 kPa of applied stress the consolidation improved by 30% even at 10 V. The coupled approach lowers the corrosion potential to negligible levels (≤0.2% by weight).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".