Quantitative Simulation of Human Evacuation Dynamics under Visibility Impairment in Indoor Fire Scenarios
Bibliographic record
Abstract
This study quantitatively analyzes the impact of visibility degradation caused by smoke spread on human evacuation behavior during indoor fires. Hence the reaction-based evacuation model outlined in the New Zealand Fire Safety Verification Method (C/VM2), simulations were conducted to observe how evacuees respond under conditions of gradually decreasing visibility due to increasing smoke concentration. Specific behavioral changes were examined, including Response Time, Route Selection, Avoidance Behavior, and Situational Awareness. For the purpose of this study, an indoor environment was modeled, and smoke spread was visualized to design an evacuation model that reflects visibility constraints. The analysis revealed that once smoke concentration exceeds a certain threshold, evacuees face significant visual impairment, leading to delay in evacuation and increased risk of collisions. Furthermore, the Presence of Smoke Control System, the Status of Door Opening, and the Configuration of Evacuation Route were found to be closely related to visibility during fire events. It emphasizes the importance of incorporating visibility-based strategies into evacuation planning alongside physical architectural considerations in performance-based fire safety design. It also suggests the potential for integrating psychological response factors and AI-based predictive systems in future research.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".