Impact of Beater Shape in Mixing Test to Determine Clogging Potential
Bibliographic record
Abstract
Abstract Clogging is a common phenomenon in tunnel boring machine (TBM) excavations, and cutterhead shape can influence clogging potential. In this research, a mixing test with a newly proposed index has been introduced to assess clogging potential. Five mixtures—composed of different percentages of bentonite and kaolin—were employed to simulate soil conditions. For each mixture, samples with five different water contents (ranging between the plastic and liquid limits) were employed in the mixing test. Three different beater shapes were used to investigate the effect of beater shape on mixing test results. The ratio (A′/A) between the open area of the beater (A′) and the entire surface area of the beater with no opening (A) was introduced to quantify the difference in beater shape. The Hobart mixer used in the research is 18.9 liter (20-qt). Two indexes were used to analyze mixing test results: the weight of soil stuck to the beater per unit area (W/Ac) and the weight of soil stuck to the beater (GB). The results indicate that both W/Ac and GB increase with increasing bentonite content in the mixture. However, there are no clear trends that can be observed in GB for different beater shapes. The variation of maximum GB is within 0.5 kg when A′/A increases from 0.52 to 0.7 for the 5 clay mixtures, showing a difference of 12 %. It is concluded that W/Ac increases dramatically with an increase in the open area of the beater, whereas GB only varies slightly. Undoubtedly, the shape of beater used cannot be ignored for tests to assess clogging potential. Furthermore, W/Ac is a good indicator to detect the impacts of beater shape on mixing test results.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".