Quantifying Multifactor Effects on Mud Cake Formation Risk for a Tunnel Boring Machine with the Analytical Hierarchy Process
Bibliographic record
Abstract
Tunnel boring machines often encounter clogging during excavation in strata with rich clay content. The clogging can damage the cutterhead and interrupt excavation. Cutting tool clogging, also known as mud cake formation, has a strong effect on excavation work efficiency. While current studies are focusing on the mechanism of clogging, engineering practice still heavily relies on qualitative empirical judgement. To quantitatively assess the risk of mud cake formation, we carefully selected 22 influential parameters to reflect the effect of geological, machinal, and operational risk factors in the tunneling process and established a rubric for risk factor contributions using the analytical hierarchy process (AHP). The results imply the liquidity index, plasticity index, cutterhead torque, and total thrust force contribute to around 45% of the total influence on mud cake formation, while machinal factors are less influential than geological and operational factors. We verified the framework with a tunnel section from the Guangzhou Metro Line 22 that had mud cake formation reported. Rings labelled as high risk for mud cake formation by our framework concurred with those rings with mud cake actually observed. Project log and operational parameter variations were incorporated to explore the connection between mud cake formation and treatment.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".