Sensitivity and Uncertainty Analysis of a Fire Spread Model with Correlated Inputs

Sensitivity and uncertainty analysis is a very important tool to identify and treat model uncertainties in quantitative fire risk analysis. An existing Fire Spread model with correlated input variables are presented for sampling-based sensitivity analysis, and selected input variables include fire growth rate, fire resistance rating and its standard deviation, fire load density and its standard deviation. A sampling approach is proposed to deal with the correlated structure of input variables, which introduces a noise term and can transform correlated input variable structure into an independent one. Furthermore, sensitivity analysis of input variables of fire spread model is performed and an order of variable sensitivity is given. Results show that fire resistance rating and its standard deviation are two very important input variables while standard deviation of fire load density is the least sensitive parameter. Further discussions are provided on the effectiveness of the sampling technique and the use the results of the analysis.