An Intelligence Approach to Predict Fire Flame Length under Tunnel Ceiling

Various analytical models have developed to determine fire flame length under tunnel ceilings during fire emergency based on the regression and dimensional analysis. Artificial intelligence techniques are now being used as an alternate to statistical techniques. In this study, the artificial neural network (ANN) is applied to forecast fire flame length in tunnels. Moreover, particle swarm optimization algorithms were used for ANN training in order to overcome very slow convergence and easy entrapment in a local minimum of back propagation training algorithms. The model predicts flame length using Fire Heat Release Rate, Air velocity, Tunnel Width, Tunnel Height and Tunnel Cross Section. The predictive PSO-ANN model was implemented on the MATLAB and developed based on a database including 44 data sets from large scale fire test programs. The coefficient of determination (R2), the variance account for (VAF) and the root mean square error (RMSE) were calculated to check the prediction performance of the model. The R2, VAF and RMSE indices were obtained as95.884, 99.86% and 1.05.These indices revealed that the developed model is suitable for practical use in tunnels.