M-StruGAN: An Automatic 2D-Plan Generation System under Mixed Structural Constraints for Homestays

Xiaoni Gao, Xiangmin Guo and Tiantian Lo ()
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Xiaoni Gao: Harbin Institute of Technology, Shenzhen 515100, China
Xiangmin Guo: Harbin Institute of Technology, Shenzhen 515100, China
Tiantian Lo: School of Design, Jockey Club Innovation Tower V1002e, The Hong Kong Polytechnic University, Hunghom 999077, Hong Kong

Sustainability, 2023, vol. 15, issue 9, 1-19

Abstract: Existing methods for generating 2D plans based on intelligent systems usually require human-defined rules, and their operations are complex. GANs can solve these problems through independent research and learning. However, they only have generative design research based on a single constraint condition, and whether they can generate a qualified design scheme under many constraints is still unclear. Therefore, this paper develops the M-StruGAN generative model based on the structural design framework of a GAN. Its application research is extended to the 2D-plan layout generation of homestay based on the constraints of hybrid structures, and the feasibility of the method is comprehensively verified through three aspects: image synthesis quality assessment, scheme rationality assessment, and scheme design quality assessment. Experimental results show that the quality of the drawings generated by M-StruGAN is qualified, designers have a high degree of acceptance of the design results of M-StruGAN, and M-StruGAN completed the learning of the critical points of the 2D layout. Finally, through the human–computer interaction application of M-StruGAN, it can be found that compared with traditional design methods, M-StruGAN based on pix2pixHD has high-definition image quality, higher design efficiency, lower design cost, and more stable design quality.

Keywords: homestay; 2D-plan; machine learning; GAN; Pix2pixHD (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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