| PHYSICS AND ENGINEERING MECHANICS |
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Risk assessment model for building fires based on a Bayesian network |
| SHU Xueming1,3, YAN Jun2, HU Jun1, WU Jinjin1, DENG Boyu1 |
1. Department of Engineering Physics, Institute of Public Safety Research, Tsinghua University, Beijing 100084, China;
2. China Institute of Industrial Relations, Institute of Safety Engineering, Beijing 100048, China;
3. Beijing Key Laboratory of City Integrated Emergency Response Science, Beijing 100084, China |
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Abstract The development of building fires was divided into four stages for risk assessment as fire initiation, fire alarm, fire behavior, and fire spreading based on fire engineering theory with analyses of the main risk assessment parameters of each stage. The dynamic risk assessment model was based on a Bayesian network. A sensitivity analysis was then used to evaluate the influences of key parameters on the fire risk. Two typical buildings were then used as examples to evaluate the risk at each fire stage and the overall risk. The results illustrate how the building fire risk is a dynamic process with different risk and impact parameters in each stage. The model nodes and dependencies constitute a causal network. The evaluation model can effectively combine large amounts of fire data collected by a building fire monitoring terminal using artificial intelligence analyses. This research can effectively improve building fire safety management.
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| Keywords
safety engineering
building fire
risk assessment
big data
Bayesian network
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Issue Date: 03 April 2020
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2020,
Vol. 60
