Fire Egress Simulations Based on Human Behavioural Patterns
Bibliographic record
Abstract
Fire safety is essential in all buildings. Hazardous fires can result in injuries, death, and property damage. Necessary and appropriate regulations/policies and building designs for preventing fires from starting in buildings should be in the first place. However, practical and efficient evacuation plans need to be developed in the case of fire. Developing such plans requires an understanding of human behaviours during fires. Human behaviours during fires have been studied using various methods such as interviews, video footage, full evacuation demonstrations with human participants, virtual reality, and computer simulations. This paper uses computer simulations to test fire evacuations using an agent (evacuee) modelled based on actual human behavioural patterns in the case of fire. The paper presents how the evacuee model was developed by employing human instincts. The model was then tested and verified by simulating it in a maze-like environment. Finally, the paper showcases the model used to evaluate an actual building to see how a real person would behave in the case of fire in the building. The model can be used to evaluate the fire safety of various buildings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".