Investigating the role of bentonite on the conditioning of muck during tunneling through marly limestone with EPB machines
Bibliographic record
Abstract
With advancements in manufacturing technology, Earth pressure balance (EPB) machines are successfully applied in coarse-grained soils and even all types of rocks. The performance of EPB machines, whether in soil or rock, significantly depends on the proper conditioning of the excavated materials. This study aims to investigate the role of bentonite on the optimal conditioning of graded marly limestone grains, considering their mineralogical properties. To achieve this goal, 98 slump tests and 9 under-pressure permeability (UPP) tests were conducted to evaluate the effectiveness of bentonite in improving workability and reducing permeability of the conditioned muck (marly limestone). The slump test results indicate that when the fine content ( CF) in the samples is 4.5%–8.5%, bentonite slurry concentration ( CB) is about 7.5%–10%, and bentonite slurry injection ratio is around 30%–40%, conditioned materials turn into an optimal plastic paste. Additionally, the results show that optimally conditioned mixtures obtained by adding bentonite suspension exhibited significant resistance to water infiltration under high pressures (up to 0.7 MPa) during the 9 UPP tests.
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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".