Electrokinetic Strengthening of Marine Clay Adjacent to Offshore Foundations
Bibliographic record
Abstract
Skirted foundations have been used increasingly to provide uplift resistance and to carry structural loads in offshore structures. When soft clay soils are encountered in a site, the soil shear strength is one of the major concerns in the design of skirted foundations with respect to the bearing capacity. Originated from the problem facing offshore engineering practice, this study is focused on electrokinetic strengthening of soft marine clays adjacent to skirted foundations. A series of laboratory electrokinetic experiments was conducted in a natural marine clay. A steel plate was embedded in the soil during the electrokinetic treatment to simulate part of a skirted foundation. The design, execution and results of the electrokinetic tests are reported. The results show that the undrained shear strength of the soil around the embedded steel plate was increased considerably after the electrokinetic treatment. It is also evidenced that the soil shear strength was further increased with time after the electric field was withdrawn, attributable to electrokinetics induced soil particle cementation during post-treatment ionic diffusion. In order to obtain uniform strength increase between the electrodes, to reduce energy consumption and to prolong the service life of electrodes, the effects of polarity reversal and current intermittence under a constant applied voltage were also investigated.
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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".