Tunnel-Boring Machine Positioning during Microtunneling Operations through Integrating Automated Data Collection with Real-Time Computing
Bibliographic record
Abstract
This research aims to develop an automated and cost-effective solution to guide the advance of a tunnel-boring machine (TBM) during microtunneling and pipe jacking operations. Pros and cons of currently available TBM guidance systems are evaluated. A simplified TBM guidance system is proposed based on integration of automated data collection with real-time computing. The TBM’s position in terms of point coordinates is continuously and automatically surveyed by a robotic total station, thus making it feasible to derive any line and level deviations from as-designed tunnel alignment in real time. Furthermore, given the coordinates of three observation points on the TBM, the attitudes of the TBM, which are described by three rotation angles of yaw, pitch, and roll, can be determined by a vector observation algorithm. Monte Carlo simulation was conducted to assess errors of point positioning and attitude determination by the proposed solution. For concept proving and application demonstration, a hardware-software integrated prototype system was developed in house and validation experiments were successfully conducted in terms of: (1) automated surveying of multiple targets; (2) attitude determination for a moving object that mimicked a working TBM; and (3) field installation and testing based on an ongoing project.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".