An Evacuation Navigation Model Considering Fire's Impact on Escape Capability of Evacuees<sup>*</sup>
Bibliographic record
Abstract
An evacuation navigation model incorporating a fire's detrimental effect on evacuees' escape capability is presented. Fire is one of the most common hazards in buildings that severely impacts evacuees' escape capability. A fire dynamics model is used to simulate dynamics of a fire at various locations in a building. An index, called Capability Deterioration Rate (CDR), is introduced to model how fire effluents, such as smoke, heat, and toxic gases, impact escape capability spatiotemporally. This index is incorporated into a modified Dijkstra algorithm by changing weights of navigation edges. An agent-based model taking into account the fire dynamics and escape capability is used to simulate an agent's evacuation from a location to minimize the total travel cost from the location to an egress door. A three-floor building is used as a case study to demonstrate the application and effectiveness of the proposed model. The proposed model can be used to evaluate and select the best navigation plan in terms of escape capability under various fire scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".