Automated Two-Story Housing Floor Plan Generation Using Generative Adversarial Networks

Abstract

Automating the generation of two-story housing floor plans has emerged as a significant area of focus in architectural design research, driven by the need to enhance efficiency, creativity, and functionality in the design process. This study introduces a GAN-based framework for the automated generation of two-story housing layouts, incorporating architectural constraints such as functional zoning, multi-level connectivity, open-plan configurations, and visual relationships. By leveraging advanced deep learning techniques, the proposed framework achieves a balance between design creativity and practical functionality, addressing the unique challenges posed by multi-level spatial arrangements. The results demonstrate the model's ability to generate diverse and coherent floor plans that effectively meet the complexities of two-story layouts. This research underscores the transformative potential of deep learning models in architectural design, while acknowledging existing limitations in managing multi-level spatial relationships and user interaction. With continued advancements, AI has the potential to play a pivotal role in supporting architects-optimizing workflows, enabling creative exploration, and fostering user-centered, innovative designs. Ultimately, this work sets the stage for further progress in automated multi-story housing design, paving the way for a more collaborative and technology-driven architectural future.