Quality assessment of residential layout designs generated by relational Generative Adversarial Networks (GANs)

Keundeok Park, Semiha Ergan, Chen Feng

Research output: Contribution to journalArticlepeer-review


Architectural design, a complex optimization process, employs computational design tools to converge to a subset of design options within a large design space. Recent efforts incorporate deep learning to replace traditional hard-coded rules, yet the nascent and opaque nature of data-driven architectural design requires evaluating its alignment with implicit architectural requirements. This paper assesses whether current data-driven image generation models comply with these non-explicit, domain-specific requirements. Over 80,000 architecturally created floor plans were compared to a similar number of floor plans generated by the authors using the state-of-the-art generative design models. This assessment was based on metrics such as the distribution of space sizes based on their shared/private nature, visibility based on privacy requirements, and room connectivities. The employment of Mann-Whitney U tests and descriptive statistics revealed significant disparities, indicating a gap in the generative design models' understanding of the nuanced, latent rules present in real-world data. These findings highlight the need to develop domain-specific metrics to evaluate the true performance of generative design models, offering insights that will aid in the adoption and advancement of data-driven algorithms in the architecture design domain.

Original languageEnglish (US)
Article number105243
JournalAutomation in Construction
StatePublished - Feb 2024


  • Architectural design
  • Deep learning
  • Floor plan generation
  • Generative adversarial network (GAN)
  • Generative design

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


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