Design-aware optimization of 2D skeletal structures using conditional generative adversarial networks (cGANs)

Abstract

This research presents a novel diffusion-based cGAN framework for the automated optimization of 2D skeletal structures with Eurocode 3-compliant steel Circular Hollow Section (CHS) assignments. Traditional methods like SIMP are computationally intensive and require specialist knowledge, limiting their practical use. To overcome these challenges, the study develops a hybrid deep learning approach that combines the adversarial learning of cGANs with the progressive refinement of diffusion models, enabling efficient and codecompliant structural design generation. The methodology involves a three-phase process: first, generating a comprehensive dataset through topology optimization, skeletonization, frame extraction, and code-compliant section assignment; second, training an advanced diffusion-based cGAN that conditions on user-defined parameters and iteratively denoises outputs to produce high-fidelity 512×512 structural layouts; finally, identification of sections assigned in the structural layout. Experimental results show that the proposed framework is approximately 50,000 times faster than traditional FEA-based methods and achieves perfect code compliance and higher prediction accuracy than existing deep learning models. This work significantly advances both artificial intelligence and structural engineering by automating highquality, regulation-ready design generation.