The article presents the results of numerical analyses and experimental research concerning the neural evaluation of the mass moisture content U_mc of brick walls in historic buildings. For the purpose of training, testing and validating artificial neural networks, a representative data set was built on the basis of tests of the moisture content and salinity of brick walls in ten historic buildings. The article presents two structures of artificial neural networks that are most useful for the neural evaluation of the mass moisture content, which were selected on the basis of the conducted analyzes. The results of comparative applications of all analysed algorithms were also included in the paper. High R² values for learning, testing and validation using artificial neural networks prove the credibility of the results. This means that the proposed method can be used in construction practice to assess, after practical verification, the moisture content of brick walls.

本文介绍了历史建筑砖墙质量含水率U _{mc神经评估的数值分析和实验研究结果。}为了训练、测试和验证人工神经网络，在十座历史建筑中砖墙的水分含量和盐度测试的基础上，建立了一个具有代表性的数据集。本文介绍了两种对质量水分含量的神经评估最有用的人工神经网络结构，它们是根据所进行的分析选择的。论文中还包括了所有分析算法的比较应用结果。高R ²使用人工神经网络进行学习、测试和验证的值证明了结果的可信度。这意味着所提出的方法可用于建筑实践，经过实际验证后，可以评估砖墙的含水量。