The brick-and-mortar structure inspired by nature, such as in nacre, is considered one of the most optimal designs for structural composites. Given the large number of design possibilities, extensive computational work is required to guide their manufacturing. Here, we propose a computational framework that combines statistical analysis and machine learning with finite element analysis to establish structure–property design strategies for brick-and-mortar composites. Approximately 20,000 models with different geometrical designs were categorized into good and bad based on their failure modes, with statistical analysis of the results used to find the importance of each feature. Aspect ratio of the bricks and horizontal mortar thickness were identified as the main influencing features. A decision tree machine learning model was then established to draw the boundaries of good design space. This approach might be used for the design of brick-and-mortar composites with improved mechanical properties.

受自然启发的砖混结构（例如珍珠母）被认为是结构复合材料的最佳设计之一。考虑到大量的设计可能性，需要大量的计算工作来指导其制造。在这里，我们提出了一个计算框架，该框架将统计分析和机器学习与有限元分析相结合，以建立实体复合材料的结构-属性设计策略。根据几何模型的失效模式，将大约20,000个具有不同几何设计的模型分类为好和坏，并对结果进行统计分析以发现每个特征的重要性。主要影响因素是砖的纵横比和水平砂浆厚度。然后建立了决策树机器学习模型，以画出良好设计空间的边界。该方法可用于设计具有改善的机械性能的砖瓦复合材料。