Improvement of the load-carrying capacity of offshore skirted foundations by electrokinetics
Bibliographic record
Abstract
Originating from the problem facing offshore foundation engineering, the present study is focused on using electrokinetics to enhance the load-carrying capacity of skirted foundations embedded in soft marine deposits. An experimental study was carried out in a model tank having dimensions of 150 cm × 75 cm × 70 cm. The experiments were conducted on the Welland River sediment mixed with a high salinity solution that simulates the composition of seawater. A steel cylinder of 320 mm in diameter was embedded in the sediment to represent a skirted foundation. Electrodes were installed around the steel cylinder, and a voltage of 5.2 V was applied over 28 days with polarity reversal. The load-carrying capacity of the steel cylinder and the undrained shear strength of the adjacent soil were measured after the electrokinetic treatment. The effect of electrokinetics is evaluated by comparing a series of test results performed on the untreated and treated soil. The load-carrying capacity of the steel cylinder and the undrained shear strength of the adjacent soil increased up to three times after treatment. The study also shows that electrokinetics can regain and further enhance the load carrying capacity of the embedded skirted foundation model after failure. With further development, the technology has the potential to be applied in offshore engineering practice to increase the load-carrying capacity of skirted foundations installed in soft clayey sediments.Key words: electrokinetics, skirted foundations, soft marine clay, load-carrying capacity, soil improvement.
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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".