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 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".