Predicting heat release properties of flammable fiber-polymer laminates using artificial neural networks

Heat release rate is an important fire reaction property used to quantify the flammability of composite materials in fire. In this study, an artificial neural network (ANN) model was developed to predict the heat release properties of composites. The ANN model was trained using 10,419 data points for heat release rate extracted from the results of cone calorimetry tests performed on 14 sets of composite laminates. Two machine learning algorithms of Multiple Linear Regression (MLR) and Bayesian regularized artificial neural network with Gaussian prior (BRANNGP) are compared. The composites used to demonstrate the predictive accuracy of the ANN model were phenolic-based laminates containing different types and amounts of flame retardant additives. The BRANNGP model is capable of predicting the heat release rate-time curve, peak heat release value and total heat release of the composites. In addition, the BRANNGP model with outlier-elimination strategy can estimate with good accuracy the complex non-linear relationship between heat release rate and heat flux exposure time without considering the mechanistic interactions between the input and output parameters.