Mechanical properties of clayey soil relevant for clogging potential
Bibliographic record
Abstract
Due to its advantages, including reduced operation time and cost, tunnelling with boring machines in urban areas has become a popular technique over hand-tunnelling or open-trench excavations. However, tunnelling in clayey soil poses a great challenge since the mechanical and physical properties of clays cause different issues, one of which is clogging. To solve this problem, engineers have treated the soils with different soil conditioners in order to change their rheological properties, improve their manageability and eliminate undesirable characteristics. However, the root of this problem is the lack of understanding the phenomenon and characterising it on the framework of soil mechanics. By considering the use of polymers, this paper aims to provide information regarding the theory involved in this mechanism and the factors influencing clogging potential. The authors present a systematic study of the physical and mechanical properties of plain soil and conditioned soil, which includes a direct shear test, vane shear test and Atterberg limits test. Results show that the initial water content, roughness of shear plate and percentage of additive have a significant effect on the clogging potential. Additionally, the results are plotted on an empirical diagram to understand the clogging potential by relating the clogging potential to soil properties.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".