| id |
ecaade2024_215 |
| authors |
Park, Hyejin; Gu, Hyeongmo; Hong, Soonmin; Choo, Seungyeon |
| year |
2024 |
| title |
Comparison of GAN-based Spatial Layout Generation Research Focusing on AIBIM-Spacemaker and GAN-based Prior Research |
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 539–548 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.539
|
| summary |
Recent advancements in Large Language Models (LLM) and the emergence of ChatGPT are rapidly progressing Generative AI models, suggesting the possibility of AI replacing human creative activities. In architecture, where outcomes depend on human creative thinking, the pre-planning stage is crucial. Architectural planning involves decisions on mass, space layout, and space program, aiming for optimal design with a significant impact on subsequent stages. Creating a client-centric design within a given time prompts architects to search for diverse reference materials. However, finding comparable spatial layouts is challenging due to the predominant focus on materials, construction methods, and details. This study introduces AIBIM-Spacemaker, a Generative Adversarial Network (GAN)-based program we developed for generating spatial layouts through graphical composition of space programs. Focusing on a house with limited space usage but versatile layouts, the study collected 10,000 raster-based floor plan images, creating a training dataset annotated for spatial elements. Training this dataset using the YOLO model enabled automatic extraction of vector-based data representing spatial relationships from raster-based images. A GAN trained on this data resulted in AIBIM-Spacemaker, allowing users to create diverse spatial layouts. Executing a graph with nodes representing spaces and edges denoting relationships between doors and windows using the trained GAN produced varied spatial layouts. Verification, comparing actual ground truth values, GAN-generated outcomes, and architect-provided values confirmed the program's effectiveness in the planning stage. Performance was verified by comparing the program, learning method, dataset, and results developed in this study with previous studies on GAN-based spatial layout generation. This study identifies the potential for AI-based spatial layout generation, enhancing planning efficiency and contributing to intelligent design automation, with anticipated positive impacts on planning task efficiency. |
| keywords |
Space Layout Generation, Space Program, Generative Adversarial Networks(GNN), You Only Look Once(YOLO), Pre-design stage |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (2,590,346 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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