Integrating Bones and Structural Design In Architectural Biomimicry Using Deep Learning and Finite Element Analysis

Architects’ understanding of natural forms has influenced the development of freeform architecture, yet challenges remain in fully integrating the rationality of natural structures into design. This study addresses these challenges using a framework based on Pix2PixHD-Generative Adversarial Networks (GANs). By studying images of the junction between human zygomatic and frontal bones, we have created a novel method for applying image-based deep learning to biomimetic architectural design. We trained on 500 sets of samples and randomly generated 10 results, which were then analyzed and validated using Finite Element Analysis (FEA). The results indicate that our trained model can efficiently generate biomimetic curves according to specific design requirement, and the resulting bone-like structures demonstrate advantages in load distribution and volume. This study innovatively combines deep learning with FEA, creating an efficient biomimetic design framework and offering new perspectives for digital design and biomimicry.