Intelligent design of shear wall layout based on attention-enhanced generative adversarial network

Abstract

The preliminary layout design of the shear wall is critical for the design of reinforced concrete shear wall structures. Deep learning-based methods can learn design experience from existing design data and generate new designs efficiently. However, existing research is insufficient for the design of the local layout of shear walls in critical zones. The attention mechanism can identify the critical zones in an image that must be focused on and provides a new potential solution for improving the local design of shear wall structures. Therefore, this study proposed an attention-enhanced generative adversarial network model, named StructGAN-AE, for the intelligent design of shear wall structures. Additionally, a pre-training method was proposed to overcome the limitation of data shortage. Case studies showed that, compared with existing models, StructGAN-AE could generate a more reasonable local layout of shear walls in critical zones, such as elevator shafts, and the rationality of the generated shear walls was improved. The comprehensive evaluation metric of shear wall design results was improved by up to 30%. In terms of structural performance, the shear wall structures designed using StructGAN-AE demonstrated better anti-torsion performance, closer to engineers’ design, and lower consumption of concrete and steel than those designed using existing methods.