Two 4D Models Effective in Reducing False Alarms for Struck-by-Equipment Hazard Prevention
Bibliographic record
Abstract
Over the past decade, several smart and automated systems have been developed to address the issue of struck-by hazards in construction—that is, workers on foot struck by equipment or equipment struck by equipment. False alarms (false positives and false negatives) are common in such systems, but methods for limiting struck-by hazards have not yet been thoroughly studied or tested for real-world implementations. This study presents two novel four-dimensional (4D) [time and three-dimensional (3D) space] models, a time-sphere model and a time-cuboid model, that are effective in reducing the rate of false alarms. In each developed 4D model, (1) entities’ state information, including 3D position, orientation (roll, pitch, and yaw), and velocity, is acquired and analyzed over time; and (2) the hazardous area around equipment or workers is represented by a sphere or a cuboid with the warning distance adjusted and updated according to the entities’ collected state information; and (3) unsafe-proximity query rules identify and predict contact collisions using relative position, moving direction, speed, and a pairwise 3D unsafe-proximity query. The effectiveness of the developed 4D models was evaluated through simulation and field experiments; however, the data were not wirelessly communicated because the focus of the study was on development, analysis, and comparison of two models for safety hazard identification. The obtained false positive and false negative rates indicate that the two developed 4D models have a strong capability for reducing false alarms. The obtained reduced alarm percentages imply that on average 65% of the alarms triggered by the most prevalent method can be averted by using the time-sphere model and 81% can be reduced by using the time-cuboid model. Furthermore, three major categories of findings are summarized: model comparison, model analysis, and the relationship between alert zone dimensions and model performance. The developed rigorous 4D models can also be employed for several types of contact collision that involve temporal and permanent site facilities, materials transported in air, and equipment and workers on foot. Reduced false alarms will improve construction safety, productivity, and mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".