StructDiffusion: End-to-end intelligent shear wall structure layout generation and analysis using diffusion model

Shear wall structures are widely used in high-rise buildings. However, the design process of shear wall structures suffers from inefficiencies. This paper introduces StructDiffusion, an end-to-end intelligent system for shear wall layout generation and analysis. The proposed approach tackles the layout design task by employing a novel diffusion model architecture to generate conditional images. Key components of the proposed method include pretrained diffusion models, ControlNet for conditional control, and LoRA for efficient adaptation. Notably, the method allows for architectural plan images and basic textual design conditions as inputs, enabling manipulation of the generated layouts by adjusting attributes such as building height and seismic intensity. To evaluate the quality of the model's design swiftly, this paper presents a comprehensive evaluation framework that incorporates perceptual and structural validity metrics. Through experimental analyses, this paper demonstrates the effectiveness of the proposed model in generating layouts, surpassing the capabilities of GAN-based methods. Furthermore, our study investigates model-specific parameter configuration, text-guided capabilities, code compliance, and computational efficiency. StructDiffusion significantly enhances the automation and intelligence of structural engineering workflows. The framework effectively addresses challenges associated with instability, data scarcity, and limitations in assessment. This pioneering application of diffusion models opens up promising avenues for advancing data-driven structural design.