Irradiation embrittlement of steel pressure vessels is an important consideration for the operation of current and future light water nuclear reactors. In this study we employ an ensemble of artificial neural networks in order to provide predictions of the embrittlement using two literature datasets, one based on US surveillance data and the second from the IVAR experiment. We use these networks to examine trends with input variables and to assess various literature models including compositional effects and the role of flux and temperature. Overall, the networks agree with the existing literature models and we comment on their more general use in predicting irradiation embrittlement.

钢制压力容器的辐照脆化是当前和未来的轻水核反应堆运行的重要考虑因素。在这项研究中，我们采用人工神经网络的集成，以便使用两个文献数据集（第一个基于美国监视数据，第二个来自IVAR实验）来提供脆化的预测。我们使用这些网络来检查输入变量的趋势，并评估各种文献模型，包括成分效应以及通量和温度的作用。总的来说，这些网络与现有的文献模型相符，我们对它们在预测辐照脆化中的更广泛使用进行了评论。