Fire poses risks to our communities on many different levels. Fires can destroy homes, costs billions of dollars in damage, and lead to personal injuries or death. Understanding fire risk is integral in employing preventive methods to managing the negative effects of fires. Our research includes developing methods to quantify the risk fire pose on our communities using statistical analysis and fire modeling. The goal of the research to provide decision makers resources to effectively manage fire risks and reduce the impact of fire on our communities. 

Interactive room layout survey
flow chart showing the effect of uncertainties on fire risk assessment
FireCares screen shot of Austin Fire Department commuity risk

Screen capture of the risk assessment webpage for the community covered by the Austin Fire Department. Fire departments can use this risk assessment to identify and address community needs.

Risk assessments are affected by uncertainties in available data. For example, variations in room layouts and room furnishing will affect the rate of fire spread, which is an important consideration in fire risk assessments. 

Screen capture of tan interactive online survey developed by UTFRG to identify probable bedroom layouts. The purpose of the survey is to collect data to correlatte furniture layouts with community demographic. 



Researcher: Tyler Buffington


The Fire-Community Assessment Response Evaluation System, or FireCARES, is a "big data" analytics tool that provides community leaders information to evaluate fire service resource allocation. Adequate fire department coverage can significantly reduce the fire risk in a community. Fire department coverage not only depends upon physical distance from fire departments, but also how quickly structural fires grow. Modeling the spread of fires inside buildings is difficult due to the large number of variables that can significantly affect the outcome. Furniture layouts, housekeeping habits, and home geometries all affect fire damage outcomes. To account for all of these many possible fire scenarios, room layout data from surveys are used in predictive fire models to generate statistical distributions of fire damage outcomes. These results are then correlated to individual community demographics and used as benchmarks for evaluating community fire risk.


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Researcher: Austin Anderson


Recently, due to concerns about sustainability, green construction, and use of “natural” materials, existing fire safety measures are increasingly requiring greater justification for their presence and new approaches are being discussed frequently. When considering old and new fire safety measures, studying the viability of their adoption with an aim to minimizing losses and maximizing safety is paramount. Unfortunately, these studies often focus either exclusively on the statistics at the regional level or physical modeling at the building scale. The goal of this methodology is marry these two approaches by using state level fire loss data to inform and validate the development of a physical fire model that can also receive input from small scale testing. A final step is developing a way to scale results from such a model to predict community scale risk impact.

Outline of the proposed methodology using statistical data, fire modeling, and experimentl data to quantify the impact of flame retardants on fire risk in residential homes​

Evaluation of uncertainty in known vs unknown factors such as area of origin and item first ignited for incidents of residential fires recorded in fire databases. 

Statistical evauation of uncertaity in fire databases
Flow chart of statistical methodology to assess fire rik