Automation System Design and Lab Testing to Facilitate Tunnel Boring Machine Guidance in Construction of Large-Diameter Drainage Tunnels
Why this work is in the frame
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Bibliographic record
Abstract
Construction of large-diameter drainage tunnels requires stringent line and grade tunnel alignment control in order to carry sewer and storm water in municipal areas. Tunnel boring machine (TBM) has been extensively applied to improve tunneling productivity and safety performances on drainage tunnel projects. The current practice for TBM guidance largely relies on the traditional laser system, which however falls short of accuracy and reliability. The proposed research aims to develop an automation solution to facilitate TBM guidance and as-built tunnel alignment survey in drainage tunnel construction. In contrast with our previous application of automating TBM guidance in microtunneling and pipe jacking for installing small-diameter utility pipelines, this research will address a related but more challenging problem defined in the context of drainage tunnel construction due to different construction methods and particular site constraints. An automation system is proposed, in which a robotic total station is employed to automate the continuous process of TBM tracking and positioning in the 3D underground space. ZigBee-based wireless sensor networks are applied for wireless data communication inside the tunnel. Real-time survey data are thus acquired and processed on the fly, resulting in: (1) TBM's precise coordinates in the underground space; (2) three-axis body rotations of the TBM; (3) tunneling chainage progress; and (4) line and grade deviations of the tunnel alignment. A prototype of the automation system was developed in-house and the lab testing carried out.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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