Comparative analysis of intelligent retrofit design methods of RC frame structures using buckling-restrained braces

Abstract

The development of intelligent design methods for buckling-restrained brace (BRB) retrofit schemes can effectively enhance the seismic performance of reinforced concrete (RC) frame structures to address their insufficient seismic capacity. This study further explores the two-stage intelligent design framework for BRB retrofitting by combining generative artificial intelligence (AI) and optimization algorithms. In Stage 1, generative AI models, including diffusion models, generative adversarial networks (GANs), and graph neural networks, extract features from design drawings to identify potential BRB locations. In Stage 2, optimization algorithms, such as genetic algorithms, simulated annealing, and online learning, integrated with YJK Y-GAMA software, determine the optimal placement and sizing of the BRBs. A comprehensive comparative analysis of design performance and efficiency is conducted for different algorithm combinations in both stages. The results indicate that GANs and diffusion models effectively capture both global and local design features, and genetic algorithms provide an efficient exploration of the design space. Combining these methods yields near-optimal solutions in a short time, ensuring compliance with mechanical standards and cost-effectiveness. In conclusion, this study offers valuable recommendations for the selection of generative AI methods and optimization algorithms in the design process, with the potential to promote the application of intelligent design in engineering practice.