Analytical Tunnel-Boring Machine Pose Precision and Sensitivity Evaluation for Underground Tunnelling
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Bibliographic record
Abstract
Analytical Tunnel-Boring Machine Pose Precision and Sensitivity Evaluation for Underground Tunnelling Duanshun Li, Sheng Mao, Ming Lu, Xuesong Shen Pages 1-9 (2015 Proceedings of the 32nd ISARC, Oulu, Finland, ISBN 978-951-758-597-2, ISSN 2413-5844) Abstract: Practical Tunnel-boring Machine (TBM) guidance systems usually use sophisticated instruments to obtain the orientation of the TBM. These systems require frequent calibration and work shutdown, making the procedure complex and time consuming. Besides, the majority of current TBM guidance solutions cannot provide the position of the invisible cutter head analytically including commonly used laser point tracking system. This makes it impossible to check the accuracy of the TBM positioning for ensuring the quality of the tunnel being installed in the ground. To address the problem, this research proposes an analytical approach to quantify the accuracy of the TBM position and orientation estimation based on a previously proposed survey-based TBM guidance system. The coordinates for the cutter head center are derived through error propagation based on defining geometric constraints and applying computing algorithms, such as the layout configuration of the prisms mounted on the TBM and the relative position of the cutter head center. The paper also provides a thorough analysis of the sensitivity of the solution with regards to certain configurations based on underlying formulas. To verify the results, a tunnelling experiment was also conducted based on a 1:20 scaled TBM model with its cutter head visible. The proposed method provides a valuable approach to evaluate the accuracy of the cutter head position estimation and potentially enable the operators to control and guide the tunnelling process in the field. Keywords: TBM, Navigation, Analytical, Error propagation, accuracy, sensitivity DOI: https://doi.org/10.22260/ISARC2015/0074 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it