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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".