FIFTH™: a stack based GP language for vector processing
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
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Modeling to predict flame spread and fire growth is an active area of research in Fire Safety Engineering. A significant limitation to current approaches has been the lack of thermophysical material properties necessary for the simplified pyrolysis models embedded within the models. Researchers have worked to derive physical properties such as density, specific heat capacity, and thermal conductivity from data obtained using bench-scale fire tests such as Thermo-Gravimetric Analysis (TGA). While Genetic Algorithms (GA) have been successfully used to solve for constants in empirical models, it has been shown that the resulting parameters are not valid individually as material properties, especially for complex materials such as wood. This paper describes an alternate approach using Genetic Programming (GP) to automatically derive a mass loss model directly from TGA data.