3D Laser Scanner for Tunnel Surveying and Accuracy Analysis According to Registration Method
Bibliographic record
Abstract
The tunnel is an important facility in the United States and Canada and is recognized as an infrastructure for eco-friendly urban construction among the structures that make up smart cities.Total Station Measurements and Global Navigation Satellite Systems (GNSS) have the disadvantage of long working time and impossible satellite reception for tunnel measurements.Recently, 3D laser scanners have been used in a variety of areas as a new way to improve existing surveying methods.In this study, 3D laser scanners were used for tunnel measurements to assess its usefulness.Scan data was obtained by configuring traverse using the Total Station function and compared with check points at 10 points already installed for accuracy verification.Results of accuracy evaluation compared with check points, the maximum error was within 6cm in the N, E, and H directions, indicating the plane and elevation acceptable accuracy of scale 1:1000 digital maps, and suggesting the applicability of methods using reference point performance and laser scan data.Scanning data enables continuous analysis of scan section shapes as well as cross sectional analysis.Further research can improve the accuracy of the feature registration method, which can improve the tunnel survey 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 | 0.003 | 0.003 |
| 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) | 0.004 | 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".