A diffusion-based sequential generative workflow for AI-assisted architectural and structural design of detached houses

Abstract

Traditional architectural and structural design workflows often require repetitive manual operations. Although recent artificial intelligence methods have advanced floor plan generation and structural design, most studies focus on isolated subtasks and simple residential datasets, limiting their applicability to detached houses. This study develops an AI-assisted sequential generative workflow for preliminary architectural and structural design of detached houses. For floor plan generation, a transfer learning strategy is adopted, in which the model is pretrained on RPLAN and then fully fine-tuned on a self-collected dataset of detached houses. For architectural component generation and frame column placement, binary feature tensors are constructed as condition representations to encode the results generated by upstream models. Beam layout generation, member sizing, and PKPM-based verification are further connected through existing modular structural design methods. Quantitative results show that the workflow achieves a Micro IoU of 0.6165 for floor plan generation, a Micro IoU of 0.7479 for architectural component generation, and an IoU of 0.7885 for frame column placement. Three case studies successfully passed PKPM-based seismic code verification and reduced preliminary design time by approximately 91% compared with conventional manual workflows, demonstrating the potential engineering applicability and efficiency improvement capability of the proposed workflow. It is worthy to note that the current validation is limited by the small dataset, manual refinement between stages, case-based verification, and limited seismic design scenarios.