Automated Real-Time Monitoring System to Measure Shift Production of Tunnel Construction Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The productivity of a tunnel construction project can deviate from the predicted plan due to many factors, such as equipment failure, weather conditions and unexpected soil characteristics. Early detection of such deviations can help management teams to reallocate resources and take necessary actions to maximize the productivity. The real-time monitoring of actual productivity would yield tremendous information toward this end, but such monitoring is difficult, especially with remote construction sites. Therefore, the common practice has been to periodically obtain manually generated aggregated productivity reports from sites. These aggregated reports are not available to both site and office management in real time and may lack detailed information. To avoid these drawbacks, the research presented in this paper proposes an automated tunnel construction monitoring system to measure the productivity of the tunnel construction in terms of shift production (meters/shift). This system computes the shift production in real time using time-lapsed images of a tunnel construction site and provides instant access to these reports through a secure web portal. The web portal also shows video clips of remote site activities. The reports generated by the system can be verified without obtaining any additional input from the sites. This paper describes the design of the proposed system in detail, including its principles, image processing algorithms, system architecture, and user interface details. System operation is illustrated using real examples. Validation results are presented and analyzed at the algorithmic level as well as at the system level.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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