Assessing safety in buildings and of evacuees considering fire impacts
Bibliographic record
Abstract
This study creates a fire evacuation simulator considering fire impacts on safety in buildings and of evacuees. Fire dynamics simulation, navigation graph generation, fuzzy logic system , and agent-based simulation are integrated to capture the interactions of fire, building, and evacuees. Indices are proposed to evaluate the safety in all parts of buildings and of evacuees. A fuzzy logic system is built to model exit selection of evacuees concerning fire dynamics. The contributions include: (1) introducing the edge capability deterioration index (ECDI) and path capability deterioration index (PCDI) to evaluate fire impacts on the safety of edges and paths, respectively; (2) presenting a capability ( c ) index to quantify the fire-affected safety of evacuees; and (3) developing a fuzzy logic system to model exit selection of evacuees. A three-storey building, accommodating a population of 615 individuals, is examined as a case across nine scenarios. The findings include relocating the fire from a non-critical compartment to near an exit decreases the final total c by 3.4% and increases evacuation time by 13.8%. This relocation raises the maximum ECDI at 400 s by 21.1%. They also demonstrate that the shortest path is not always the safest, as illustrated by a specific location at a particular time, in which the safest path takes 83.3% longer but has a PCDI 18.2% lower than the shortest time path. The findings signify the capability of the developed indices in assessing the safety in buildings and of evacuees spatiotemporally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".