Spiders, silks and webs are abundant in most ecosystems, suggesting that they are a significant evolutionary success. The structures they build – in particular various forms of webs – have intrigued engineers for a long time, and elucidated inspiration for new mechanical designs of de novo materials. Here we report the development of a biomateriomic neural network based constitutive model to describe the mechanical features, such as strength and toughness, of a 3D spider web depending on salient structural features, specifically: average fiber lengths, fiber orientations, web connectivity, and web density. In particular, we focus our study on the structural and mechanical properties of a Cyrtophora citricola spider web, and report a method to derive the neural net model directly from the experimental–computational mesoscale modeling, using a novel data augmentation method. Our machine learning model captures the complex biomateriomical mechanics of spider webs. More generally, approaches such as reported here can be useful to describe the intricate relationships in other hierarchical materials, and provide a basis to develop multiscale models, and bridge experimental data with computational and theoretical modeling efforts.

蜘蛛，丝和网在大多数生态系统中都很丰富，这表明它们是重大的进化成功。他们建造的结构（尤其是各种形式的网）很久以来吸引了工程师，并阐明了从头开始进行新的机械设计的灵感。在这里，我们报告了基于生物体神经网络的本构模型的开发，该模型描述了3D蜘蛛网的机械特征，例如强度和韧性，具体取决于突出的结构特征，尤其是：平均纤维长度，纤维取向，纤维网连接性和纤维网密度。特别是，我们将研究重点放在柠檬丝藻的结构和力学性能上。蜘蛛网，并报告了一种使用新颖的数据扩充方法直接从实验-计算中尺度模型导出神经网络模型的方法。我们的机器学习模型捕获了蜘蛛网的复杂生物材料力学。更一般而言，此处报告的方法可用于描述其他层次结构材料中的复杂关系，并为开发多尺度模型以及将实验数据与计算和理论建模工作联系起来奠定基础。