Rural residential layout generation and evaluation: An approach based on the Wave Function Collapse algorithm

Space layout design (SLD) is an essential process in architectural design. While residential layout generation has been widely explored, limited research has focused on rural residences. To address this gap, this study proposes a novel approach utilizing the Wave Function Collapse (WFC) algorithm to generate rural residential layouts, offering adaptability to the diverse site conditions and varying residential needs in rural areas. Firstly, we introduce a method for floor plan analysis that automatically extracts both geometric and topological features from a given dataset. We then develop a rural residential layout generation program based on WFC. Finally, the multi-criteria decision-making (MCDM) method is utilized to assess the generated layouts from perspectives of spatial rationality and usability efficiency. To validate the effectiveness of this approach, we conducted experiments using rural residential layouts from northern China as a case study. The results demonstrate that our approach can efficiently generate diverse rural residential layouts, highlighting the computational efficiency of WFC and its adaptability to varying design conditions. Our study provides architects with an intelligent tool in the early stages of rural residential design, including layout analysis, layout generation, and layout evaluation.

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Publication Details

Journal
International Journal of Architectural Computing
Published
2025-03-13
DOI
https://doi.org/10.1177/14780771251326773
Citations
2
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
7.08

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article

Rural residential layout generation and evaluation: An approach based on the Wave Function Collapse algorithm

Hanlin Li, Zhi-Gang Wang, Rui Zhang, Ye Zhang et al.
2 citations
International Journal of Architectural Computing
Computer Graphics and Visualization Techniques
7.08
article

Rural residential layout generation and evaluation: An approach based on the Wave Function Collapse algorithm

Hanlin Li, Zhi-Gang Wang, Rui Zhang, Ye Zhang, Zhen Xu, Yin Zhang
article en
2 citations

Abstract

Space layout design (SLD) is an essential process in architectural design. While residential layout generation has been widely explored, limited research has focused on rural residences. To address this gap, this study proposes a novel approach utilizing the Wave Function Collapse (WFC) algorithm to generate rural residential layouts, offering adaptability to the diverse site conditions and varying residential needs in rural areas. Firstly, we introduce a method for floor plan analysis that automatically extracts both geometric and topological features from a given dataset. We then develop a rural residential layout generation program based on WFC. Finally, the multi-criteria decision-making (MCDM) method is utilized to assess the generated layouts from perspectives of spatial rationality and usability efficiency. To validate the effectiveness of this approach, we conducted experiments using rural residential layouts from northern China as a case study. The results demonstrate that our approach can efficiently generate diverse rural residential layouts, highlighting the computational efficiency of WFC and its adaptability to varying design conditions. Our study provides architects with an intelligent tool in the early stages of rural residential design, including layout analysis, layout generation, and layout evaluation.

International Journal of Architectural ComputingVol. 24(1)
Tianjin University (CN), Sichuan Provincial Architectural Design and Research Institute (China) (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 4%
Computer Graphics and Visualization Techniques
7.08
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Rural residential layout generation and evaluation: An approach based on the Wave Function Collapse algorithm — Hanlin Li, Zhi-Gang Wang, et al. · International Journal of Architectural Computing (2025) | TGRS Research Map | TGRS