Platform Development of BIM-Based Fire Safety Management System Considering the Construction Site
Bibliographic record
Abstract
Fire at a construction site usually results in serious accidents. Therefore, fire management at the construction site is critical to decreasing possible accidents. However, conventional fire safety management can be problematic in many aspects, such as visualization, multi-stage alarm systems, and dynamic escape route optimization. To solve these issues, this paper develops a platform for a BIM-based fire safety management system that considers the construction site. The developed platform contains four subsystems: a remote monitoring subsystem, a fire visualization subsystem, a multi-stage fire alarm subsystem, and an escape route optimization subsystem. It detects the fire hazard in the early stage of the fire by the remote monitoring subsystem and transmits this information to the fire visualization subsystem for displaying. Furthermore, the multi-stage fire alarm subsystem sends warnings or alarms based on the fire’s severity. Moreover, the escape route optimization subsystem dynamically optimizes the evacuation routes by considering the actual number of people at the construction site and the potential crowding as people pass through the escapeway. Results show that this system can provide informative and on-time fire protection measures to different participants at the construction site. This study can also serve as a solution to improve fire safety management at the construction site.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".