Quantitative Testing of Fire Scenario Hypotheses: A Bayesian Inference Approach

Abstract

Fire models are routinely used to evaluate life safety aspects of building design projects and are being used more often in fire and arson investigations as well as reconstructions of firefighter line-of-duty deaths and injuries. A fire within a compartment effectively leaves behind a record of fire activity and history (i.e., fire signatures). Fire and arson investigators can utilize these fire signatures in the determination of cause and origin during fire reconstruction exercises. Researchers conducting fire experiments can utilize this record of fire activity to better understand the underlying physics. In all of these applications, the heat release rate and location of a fire are important parameters that govern the evolution of thermal conditions within a fire compartment. These input parameters can be a large source of uncertainty in fire models, especially in scenarios in which experimental data or detailed information on fire behavior are not available. A methodology is sought to estimate the amount of certainty (or degree of belief) in the input parameters for hypothesized scenarios. To address this issue, an inversion framework was applied to scenarios that have relevance in fire scene reconstructions. Rather than using point estimates of input parameters, a statistical inversion framework based on the Bayesian inference approach was used to calculate probability distributions of input parameters. These probability distributions contain uncertainty information about the input parameters and can be propagated through fire models to obtain uncertainty information about predicted quantities of interest. The Bayesian inference approach was applied to various fire problems using different models: empirical correlations, zone models, and computational fluid dynamics fire models. Example applications include the estimation of steady-state fire sizes in a compartment and the location of a fire.