Development of Virtual Laser Target Board for Tunnel Boring Machine Guidance Control
Bibliographic record
Abstract
This research aims to develop a virtual laser target board methodology for tunnel boring machine (TBM) guidance control during tunneling operations. Current practice for TBM guidance using physical laser targets is evaluated. Coupled with a fully automated TBM tracking system resulted from in-house research, the virtual laser target board program is proposed to provide an effective aid for TBM operators and field managers in making critical decisions for tunnel alignment control. Comprehensive data processing procedures are carried out to determine: (1) TBM's position in the underground space, including any registered points on the TBM, e.g. center of TBM's cutter head; (2) tunneling progress; (3) line and grade deviations of the tunnel alignment; and (4) TBM's three-axis body rotations. Field experiments on a 2.4 m diameter TBM were conducted to collect registration data for on-line processing by the virtual laser target board program.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".