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.

热释放率是一个重要的火灾反应特性，用于量化复合材料在火灾中的可燃性。在这项研究中，开发了人工神经网络 (ANN) 模型来预测复合材料的热释放特性。ANN 模型使用 10,419 个热释放率数据点进行训练，这些数据点是从对 14 组复合材料层压板进行的锥形量热测试结果中提取的。比较了多重线性回归（MLR）和具有高斯先验的贝叶斯正则化人工神经网络（BRANNGP）两种机器学习算法。用于证明 ANN 模型预测准确性的复合材料是含有不同类型和数量的阻燃添加剂的酚醛层压板。BRANNGP 模型能够预测热释放速率-时间曲线，复合材料的峰值放热值和总放热。此外，具有异常值消除策略的 BRANNGP 模型可以在不考虑输入和输出参数之间的机械相互作用的情况下，准确估计热释放率和热通量暴露时间之间的复杂非线性关系。