The intricate interplay between the microscopic constituents and their macroscopic properties for masonry structures complicates their failure analysis modelling. A composite strategy incorporating neural network (NN) and cellular automata (CA) is developed to predict the failure load for masonry panels with and without openings subjected to lateral loadings. The discretized panels are modelled by the CA methodology using nine neighbour cells, which derive their state values from geometric parameters and opening location placement for the panels. An identification coefficient dictated by these geometric parameters and experimental data is fed together as the input training data for the NN. The NN uses a backpropagation algorithm and two hidden layers with sigmoid activation functions to predict failure loads. This method achieves greater accuracy in prediction when compared with the yield line and finite elemental analysis (FEA) methods. The results attained elucidate the feasibility of the current methodology to complement conventional approaches such as FEA to provide additional insight into the failure mechanism of masonry panels under varied loading conditions.

中文翻译：

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共轭细胞自动机和神经网络方法：砌体面板的破坏载荷预测

砌体结构的微观成分及其宏观特性之间的复杂相互作用使它们的失效分析建模变得复杂。开发了一种结合了神经网络（NN）和元胞自动机（CA）的复合策略，以预测带有和不带有承受侧向荷载的开口的砌体面板的破坏荷载。通过CA方法使用9个相邻单元对离散化的面板进行建模，这9个相邻单元从面板的几何参数和打开位置放置中得出其状态值。由这些几何参数和实验数据决定的识别系数一起作为NN的输入训练数据输入。NN使用反向传播算法和两个具有S形激活函数的隐藏层来预测故障负载。与屈服线和有限元分析（FEA）方法相比，此方法可实现更高的预测准确性。获得的结果阐明了当前方法论补充常规方法（如FEA）的可行性，以提供对各种荷载条件下砌体面板破坏机理的更多了解。