Simulating the Effects of Prior Environmental Knowledge on Fire Egress
Bibliographic record
Abstract
Fire safety is a critical consideration in all buildings, as hazardous fires can lead to injuries, fatalities, and significant property damage. To mitigate these risks, it is essential to establish appropriate regulations, policies, and building designs aimed at preventing the onset of fires. In addition, practical and effective evacuation plans must be developed to ensure safety in the event of a fire. Creating such plans necessitates a thorough understanding of human behaviour during fire emergencies. Various methods have been employed to study these behaviours, including interviews, video recordings, full-scale evacuation demonstrations with human participants, virtual reality scenarios, and computer simulations. This paper focuses on using computer simulations to investigate fire evacuations through an evacuee model that is based on actual human behavioural patterns in fire situations. The model of the evacuee incorporates fundamental human instincts with various environmental cues. The evacuee model’s fire egress was simulated with different levels of prior environmental knowledge about where the exits were. The simulation experiments show that an evacuee took a shorter time to exit when the evacuee knew about more exit locations.
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 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 | 0.000 |
| 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".