Experimental Study of Soft Clay Soil Improvement by Deep Mixing Method
Bibliographic record
Abstract
The deep method (DMM) is a soil remediation method that involves on-site mixing of soil with cement and/or other materials. These compounds, which are also known as "bonding materials," can be applied dry or wet. The current study involves the construction of 13 laboratory models to examine the means of improving soft clay soil qualities through deep mixing techniques with piling foundation. In the dry condition, static loading studies on piles and DMM were carried out using tow materials, cement, and lime. The model experiments included a single pile as well as groups of piles and cement or lime columns. There were two, three, and four piles or columns in each group. The model tests revealed that deep mixing had a significant impact on increasing bearing capacity by averaged times ranging from 1.23 to 2.43 times for soft clay soil treated with single and groups of four cement or lime columns, respectively, as well as minimizing settlement by averaged percentages ranging from 33% to 89 percent. These results were comparable to those obtained using pile foundations in the same manner. The outcomes of the model tests were also evaluated in terms of group efficiency.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".