Effect of Gradation on the Permeability of Foam-conditioned Soils in Mechanized Excavation
Bibliographic record
Abstract
Tunnel excavation in a soft ground is often conducted utilizing excavation machines, including earth pressure balance (EPB) boring machines. A safe and economical excavation using this method requires adding materials such as foam and polymer to the soil inside the chamber and the tunnel face to control parameters like permeability, plasticity, shear resistance, and compressibility. Using an experimental method, the present study investigates the effects of granulation, soil moisture content, and pressure on the permeability of a soil conditioned with foam. According to the results, as the effective grain size (d10) increased from 0.1 to 0.4 mm, the permeability of the conditioned soil grew from 2.28 × 10−5 m/s to 12.3 × 10−5 m/s. A rise in the coefficient of curvature (Cc), while the percentage of the materials passing through the sieve No. 200 was kept constant, increased the permeability coefficient (ki) of the specimens since the medium-grained particles (d30) became coarser. A rise in Cc and the percentage of materials passing through sieve No. 200 resulted in an initial rise in the ki due to the lack of contribution of d30 and a subsequent reduction in it caused by the rise in fine-grained materials. The ki was also found to have inverse relationships with the uniformity coefficient (Cu) and pressure. As Cu increased from 3 to 20, the ki declined from 1.23 × 10−5 m/s to 0.71 × 10−5 m/s.
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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 | 0.001 |
| 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".