BIM-based Model Applied in the Analysis of Fire Simulation and Evacuation of a Building
Bibliographic record
Abstract
Fire causes damage to people and things.Nowadays, there are more and more buildings with complex structures.And building height also gradually increased.When the fire occurs, the escape and rescue of people are complicated.Each floor of the building has a different fire situation, which may cause more time to escape and rescue.Moreover, the situation of each fire may be different, such as the location of the fire source, so it will increase the difficulty of fire rescue.This paper is built by building information model, and works with Fire Dynamics Simulator(FDS) and Fire Dynamics Simulator(FDS) graphical user interface PyroSim.Simulate the fire situation and evaluate and analyze the temperature, visibility and carbon monoxide concentration of the building during the fire.Due to evacuation, people will be affected by some factors to escape, this paper by temperature, visibility, carbon monoxide concentration to understand the extent of the impact on the escape of people, and by the escape simulation software Pathfinder to learn the escape situation and the time required.After integrating the simulated conditions, analyze, evaluate and recommend solutions to the problems caused by these fires.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".