Efficient Accessibility Integrated Multi-Occupancy Building Layout Generation Using Constrained Diffusion Models

Designing complex, multi-occupancy building layouts requires considering complicated spatial arrangements, design constraints, and strict accessibility requirements. Despite advancements in automatic building layout generation, existing methods struggle to address the complexities of these layouts and often overlook critical accessibility features. This paper presents a novel deep learning-based approach for generating complex, multi-occupancy building layouts that meet both architectural and accessibility standards. To improve the efficiency of training, non-corridor rooms are approximated by minimum rotation rectangles, while a graph neural network (GNN) predicts the number of corners for corridors. To address the quadratic complexity of transformers, we incorporate FlashAttention, to enhance computational efficiency. Accessibility features are integrated into the model by enforcing geometric requirements, including room size ratios and maximum corner distances to account for travel distance to egress. Additionally, a distance penalty is introduced in the loss function to ensure compliance with wheelchair clearance requirements. Experimental results show that our approach outperforms baseline models in generating realistic, complex layouts while ensuring compliance with design and accessibility constraints, making it a robust solution for generating multi-occupancy building layouts.