Explosive spalling is an unfavorable phenomenon observed in concrete when exposed to heating load. It is a great potential threat to safety of concrete structures subjected to accidental thermal loads. Therefore, assessing explosive spalling risk of concrete is important for fire safety design of concrete structures. This paper proposed a popular machine learning approach, i.e., artificial neural network (ANN), to assess explosive spalling risk of concrete. Besides, the decision tree method was also used to execute the same mission for a comparison purpose. Twenty-eight groups of heating tests were conducted to validate the proposed ANN model. The ANN model behaved well in assessing explosive spalling of concrete, with a prediction accuracy of 82.1%. This study shows that ANN is a promising method for adequate classification of concrete as material resistant or not resistant to thermal explosive spalling.

爆炸剥落是混凝土在承受热负荷时观察到的不利现象。这对承受意外热负荷的混凝土结构的安全性构成了巨大的潜在威胁。因此，评估混凝土的爆炸剥落风险对于混凝土结构的防火设计非常重要。本文提出了一种流行的机器学习方法，即人工神经网络（ANN），以评估混凝土的爆炸剥落风险。此外，决策树方法还用于执行同一任务以进行比较。进行了28组加热测试，以验证所提出的ANN模型。人工神经网络模型在评估混凝土的爆炸剥落方面表现良好，预测精度为82.1％。