A data driven real-time perception method of rock condition in TBM construction
Bibliographic record
Abstract
In tunnel boring machine (TBM) construction, the presence of collapsible rock mass (CRM) can lead to accidents such as collapse and jamming. This study presents a novel CRM early warning strategy based on real-time TBM rock fragmentation data to improve safety and efficiency in CRM conditions. The strategy includes a qualitative classification model and a quantitative probability model for CRM identification. The results indicate that the distribution dissimilarity index β effectively reflect the significance of variables across CRM and non-CRM datasets. Various parameters, including TPI, FPI, WR, and AF, show discriminatory ability between CRM and non-CRM samples. In particular, the CRM-weighted index, which combines the strengths of the individual indices, achieves a distributional dissimilarity index of 1.05, significantly higher than any of the individual indices. The qualitative classification model proves effective in identifying samples from collapse areas, demonstrating ability to identify samples located in adverse geological condition. The quantitative model shows that the probability of CRM is generally higher in adverse geological area samples, particularly in zones where collapse has occurred, with a CRM probability is approaching 1. The proposed strategy provides accurate early warnings to prevent collapse accidents and represents a practical approach to improving the safety and efficiency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".