Flashover prediction - An evaluation of the application of artificial neural networks

Trying to estimate the probability of flashover occurring during a compartment fire is a complex problem as flashover depends on a large number of factors (for example, room size, air flow etc.). Artificial neural networks (ANNs) appear well suited to problems of this nature as they can be trained to understand the factors that might cause flashover. For this reason, artificial neural networks were investigated as a potential tool ibr predicting flashover in a room with known, or estimable, compartment characteristics. in order to deal with uncertainties that can exist in a model's results, a statistical analysis was employed to identify confidence intervals for predicted flashover probabilities. In addition, Monte Carlo simulation of trained artificial neural networks was also employed to deal with uncertainties in initial room characteristic estimates. This paper presents and compares these analyses and comments on the applicability of the ANN approach to flashover prediction.