Requirements of efficient deep soil mixing treatment in clayey soils: a field-based assessment of water predrilling and auger free blade effects
Bibliographic record
Abstract
Achieving a uniform/high-strength deep soil mixed (DSM) column and avoiding sticking cohesive soils to blades (i.e., entrained mixing/rotation phenomenon) requires multi-disciplinary involvement, including drilling tool configuration and mix designs. A series of 80 cm diameter DSM columns was executed in high cohesive clays using various drilling auger formations containing different numbers of free blades and with/without water predrilling phases. Data interpretation was combined with full-depth coring and rig sensor records. The outcomes highlighted that adding free blades to the auger in a proper formation (dimensions/placement/shape/stiffness) resulted in uniform columns and facilitated the drilling by reducing the drilling pressure by about 40% while all the parameters were the same. The required water discharge in the predrilling phase was formulated to aim for a water content (about 46%) beyond the liquid limit of clayey deposits (21%–44%): a decisive technique to facilitate drilling in stiff cohesive soils. A 100% increase in the strength and a 50% enhancement in uniformity indexes in the executed columns of this project were achieved only through a tuned free-blade auger (number/configuration) and an optimal amount of added water. Besides, simultaneously implementing the predrilling phase and free blades to maximize drilling quality was inevitable due to their intertwined functions. Moreover, a new practical equation has also been proposed to consider the effect of free blades on the BRN within cohesive soil layers.
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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) | 0.000 | 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".