Quantifying Generalized Residential Fire Risk Using Ensemble Fire Models with Survey and Physical Data

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Understanding and quantifying property loss fire risk is critical to enabling decision makers in the fire field to make informed decisions. For example, the impact of various decisions made by fire departments on the fire risk in their community, while qualitatively understood by most parties, is not well quantified. Lack of quantification can lead to misdiagnosis and miscommunication on the value of services that result in sub-par resource allocations that negatively impact constituencies. Additionally, the confounding effects between fire department performance and fire risk in publicly available fire data can cause standard regression models to supply counter-intuitive conclusions based upon valid correlations (due to certain critical variables that are difficult to collect and thus unobserved). A methodology is presented that utilizes United States housing, room layout, and fire incident data, as well as experimental heat release rates, material degradation rates, and thermophysical property data in conjunction with physical fire models to estimate a community-averaged extent of fire damage in homes. Housing layout data for single family residential homes were collected from the American Housing Survey. Five home categories representing 45% of U.S. homes were selected for analysis. The U.S. Fire Administration National Fire Incident Reporting System database was used to select the distribution of initial fuels and ignition locations. Survey data was taken to specify furniture layout within the homes. A total of 5167 scenarios were developed for the combinations of home geometry, first item ignited, and furniture layout. Fire evolution was predicted for these scenarios using CFAST and coupled to a pyrolysis-inspired damage model using heat flux to targets in the homes. A damage evolution probability function was constructed for the ensemble of home and furnishing layouts and ignition distributions. This damage model was exercised in a decision analysis problem to demonstrate the utility of the methodology for community-scale resource allocation. Sensitivity studies on the model are likewise performed, indicating the largest source of uncertainty to be linked to the choice of surrogate material properties when calculating home damage. Comparison to best available data is made to assess the robustness of the model.