Integrate Building Information Modeling (BIM) and Occupant Characteristics Simulator to Assess the Effectiveness of Emergency Requirements
Bibliographic record
Abstract
Building fires are a major hazard to residents, first responders, and the structural system.Rapid spread can impede evacuation, resulting in human fatalities.However, studying occupant characteristics in a burning building is unrealistic and unethical.Hence, the current data-gathering techniques employed in evacuation simulation models have constraints when capturing occupant attributes.To address these constraints, The study introduces a novel method of serious gaming that combines Building Information Modelling (BIM) with an occupant characteristics simulator with varying mobility capabilities depending on age, gender, and physical ability, using Unity3D, to simulate fire growth and evacuation duration for residential buildings with and without emergency requirements.The study reveals that occupant characteristics significantly affect evacuation time, and implementing emergency requirements can improve evacuation efficiency in fire-exposed residential buildings by up to 100%.Implementing emergency requirements reduced mortality rates from 50% to 0%, suggesting that simulation results can be used to improve building design and emergency needs assessment.
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 | 0.001 | 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".