Optimized data representation and understanding method for the intelligent design of shear wall structures

Abstract

The generative intelligent design of building structures has been a rapidly evolving field in recent years, with shear wall structures efficiently designed using generative adversarial networks (GANs). Nevertheless, using RGB images to represent structural design drawings may not accurately capture the correlations and distinctions between building components, leading to shear-wall structure design inaccuracies. To address these challenges, this study proposes an optimized data representation and understanding method for the intelligent design of shear wall structures. Specifically, a component-based feature space data representation method was introduced to achieve a more accurate description of drawing features. A feature mask was also employed to precisely locate the key target areas for generating shear walls, enhancing the model's understanding of the data features. Additionally, a GAN with global and local discriminators was established to enhance the neural network's ability to generate shear wall designs at both the global image and local component levels. Finally, a training method for a GAN with dual discriminators based on feature masks is proposed. Experimental analyses and case studies showed that, compared with existing widely used GAN-based designs, the structural designs generated by the GAN trained with the proposed data representation and understanding methods demonstrated superior consistency with engineer-designed structures and outperformed structural seismic resistance.