Data and Model Requirements for Statistically Weighted Determination of Fire Origin for Fire Forensics

University Of Texas At Austin, Austin TX

Investigators

Abstract

Fire forensic reconstruction, in many respects, is one of the most complex and error-plagued areas of forensic science. The United States civil and criminal justice systems rely on expert witness testimony in the evaluation of fault and blame in legal cases. Increasingly, these expert witnesses rely on scientific models to either explain or promote a particular hypothesis/theory of how the fire evolved. The challenge for fire investigators is that by its very nature, a fire damages the contents of the compartment and obscures signatures produced in its early evolution. Extracting statistically meaningful signatures in a fire scene requires characterizing the thermochemical damage to typical materials present in the fire environment. These damage signatures must then be connected in a self-consistent manner to large-scale fire evolution. The overarching goal of this project is to develop a rigorous statistical methodology to connect these measured data and fire signatures to fire evolution for forensic reconstruction. This provides a scientific foundation for future development of fire forensic standards and best practices. Such standards will be the basis for training of more scientifically sophisticated fire investigators. This project will evaluate the effects of model bias and data uncertainty in issuing predictions about the origin of a fire. Measurements and observations present in typical fire scenes will be distilled into mathematical terms that can be modeled and subjected to quantifiable assessments. Several tasks are required to accomplish the project goals. First, degradation of condensed phase materials will be experimentally investigated at small and large scales to characterize thermal damage. Next, stochastic damage models will be developed to describe material degradation. Concurrently, a Bayesian framework using computational models and measured data will be developed to statistically evaluate fire origin and evolution hypotheses. Finally, a validation study will be conducted in a densely-instrumented and actuator-controlled large-scale fire test room. The project will advance knowledge in fire science by characterizing property changes for condensed phase materials during fire thermal exposure, encapsulating property change features into damage models, and identifying the limits on how these damage models coupled to gas-phase fire models should be applied to fire forensic reconstruction.