LAUSR

Abstract 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.