Generative AI-Driven Design Method of Planar Inclined Shear Wall Components for Building Structures

The advancement of generative artificial intelligence (AI) has accelerated the progress of intelligent design methods for building structures. However, generative AI models struggle to design inclined shear wall components due to their limited generalization ability. To address this obstacle, this study proposes a data augmentation method for the planar layout of rotated shear wall structures. By enhancing the dataset and associated post-processing methods, the generalization ability of design methods driven by generative AI algorithms is effectively improved. Specifically, an augmented dataset by rotating non-inclined component design data was constructed, with rotation angles from 0° to 60°, to encompass inclined shear wall design scenarios. The improved generalization performance between diffusion models, generative adversarial networks, and graph neural networks was then compared. An automatic vectorization extraction method for inclined components from the generated design feature tensors is established, enabling the application of the enhanced generative AI algorithms. Moreover, algorithm performance analysis and typical case studies show that the diffusion model performs best in the inclined shear wall design task. This comprehensive analysis confirms that data augmentation significantly improves the adaptability of generative AI to inclined component design, providing a valuable reference for enhancing the generalization of data-driven AI design in building structures.