Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks

This paper demonstrates the successful implementation of an artificial neural network to accurately predict the designated thermal radiation distance for jet fire, early pool fire and late pool fire hazard consequence analysis. Specifically, integrated feedforward neural network models employing backpropagation Levenberg–Marquardt algorithm were trained using datasets obtained through separate PHAST software simulations of 450 leak scenarios of 35 common flammable chemicals. For each fire model (jet, early, late pool), there are 11 input parameters spanning both chemical parameters and release conditions. Simulation data was randomly divided into 70% training, 15% validation and 15% test sets to conduct cross-validation and provide an independent measure of predictive accuracy for the neural network models. Statistical values coefficient of determination (R2) and mean-square error (MSE) are calculated to evaluate model regression performance. All three neural network predictive models achieved considerabl...