An Explorative Methodology to Assess the Risk of Fire and Human Fatalities in a Subway Station Using Fire Dynamics Simulator (FDS)
Bibliographic record
Abstract
Subway transportation is one of the most prevalent urban transportation methods globally. Millions of people around the globe use this medium as their mode of transportation daily. However, subway stations may be highly prone to fire, smoke, or explosion accidents. The safety of people using subway stations demands a robust and practical framework to assess fire hazards and risks. This study provides a methodology to assess fire risk at a subway station. This study integrates fault tree analysis (FTA) and fuzzy analysis to conduct a comprehensive fire risk assessment. An integrated numerical model of fire temperature and fatality rate was developed using probit correlations for various fire exposure scenarios. The fire dynamics simulator (FDS) provides the probability distribution of casualties caused by fire. To demonstrate the operationalization of the model, Line 1 of the Harbin Metro, located in China, is used as a case study. Results show a probability of 42% of having fire risk in the subway station. Results reveal the highest fatality rate is 6.2% when evacuation time exceeds 200 s. The research helps us to understand the spread of smoke and temperature distribution due to a fire in a subway station. This study is helpful for fire protection engineers, safety managers, and local fire departments to develop a contingency plan to deal with fire in a subway station.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".