Quantitative assessment of building fire risk to life safety.
This article presents a quantitative risk assessment framework for evaluating fire risk to life safety. Fire risk is divided into two parts: probability and corresponding consequence of every fire scenario. The time-dependent event tree technique is used to analyze probable fire scenarios based on the effect of fire protection systems on fire spread and smoke movement. To obtain the variation of occurrence probability with time, Markov chain is combined with a time-dependent event tree for stochastic analysis on the occurrence probability of fire scenarios. To obtain consequences of every fire scenario, some uncertainties are considered in the risk analysis process. When calculating the onset time to untenable conditions, a range of fires are designed based on different fire growth rates, after which uncertainty of onset time to untenable conditions can be characterized by probability distribution. When calculating occupant evacuation time, occupant premovement time is considered as a probability distribution. Consequences of a fire scenario can be evaluated according to probability distribution of evacuation time and onset time of untenable conditions. Then, fire risk to life safety can be evaluated based on occurrence probability and consequences of every fire scenario. To express the risk assessment method in detail, a commercial building is presented as a case study. A discussion compares the assessment result of the case study with fire statistics.
Guanquan
Chu
C
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui, China. chugq@mail.ustc.edu.cn
Jinhua
Sun
S
D002056:Burns / Q000401:mortality* / Q000517:prevention & control; D005160:Facility Design and Construction; D005390:Fires / Q000191:economics / Q000517:prevention & control / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D011336:Probability; D012306:Risk; D018570:Risk Assessment; D013269:Stochastic Processes