Automated Real-Time Monitoring System to Measure Shift Production of Tunnel Construction Projects
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".