Development of a “Smart Laser Projector” for Guiding Tunnel Boring Construction
Bibliographic record
Abstract
Engineering design of a drainage tunnel specifies start/end positions, alignment, grades, and tunnel diameter/lining material. Tunnel construction faithfully transforms engineering design into the built structure in the underground space. In construction, the guidance system of the tunnel boring machine (TBM) is crucial to ensure all design specifications are met within tight tolerances. The laser-based guidance system has been the mainstream solution in practice. However, laser system setup and calibration demands experience and expertise from a survey crew and also results in frequent construction shutdown over extensive time periods. In (1) straight tunnel sections with highly restrained survey window or (2) curved tunnel sections, the line of sight between the laser station and TBM can be easily blocked. Thus, tunnel construction has to be more frequently shut down solely for the sake of calibrating or relocating the guidance system, causing substantial productivity loss. To improve productivity of tunnel construction and enhance current practice of TBM guidance, this paper introduces research and prototyping of the Smart Laser Projector system. Unlike the traditional laser system, the Smart Laser Projector does not rely on maintaining the parallelism between the tunnel alignment and the laser beam. The system calculates the as-designed coordinates of the center in the same cross section of the as-designed tunnel model and projects a laser spot at the exact as-designed position of the center of the target board. It presents the as-designed position information to guide the operator just like the conventional laser, but its operations take much less effort of the surveyors, and the same level of accuracy and reliability can be maintained. A lab test is provided to demonstrate the concept and prototype.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".