Time-Cuboid Model with Reduced False Alarms for Construction Safety
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
Struck-by-equipment hazard is a leading cause of construction fatalities. Although several proximity detection systems have been developed to alleviate the risks of this type of hazard, the frequently generated false alarms which do not represent actual hazardous situations have limited their real-world application. Therefore, the time-cuboid model is developed to enhance construction safety by preventing struck-by-equipment accidents with reduced false alarms. The time-cuboid model effectively reduces false alarms by (1) fully considering an entities’ 3D position, orientation and velocity; (2) using a dynamic and adjusted warning distance; and (3) utilizing the developed safety rules which use relative position, moving direction, speed and a pairwise 3D safety query to identify actual as well as impending spatial interferences. Simulation and a controlled field experiment were conducted to evaluate the effectiveness of the time-cuboid model. The time-cuboid model is effective in reducing false alarms as no false negatives are generated and all obtained false positive rates (FPRs) are zero which are much lower than the FPRs of the prevalent proximity detection method (62.1% averagely). The reduced alarm percentages (RAPs) indicate that at least 50.2% alarms generated by the prevalent method can be avoided by the time-cuboid model. Reduction of false alarms contributes to enhancing construction safety, mobility and productivity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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