Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN)
Document Type
Article
Publication Date
7-1-2022
Abstract
A modular housing design entails an expensive and time-consuming process including iterative modification steps for satisfying various project and modular construction requirements. In addition, the fulfillment of all functional requirements within a limited budget in the modular housing design process remains elusive. A lack of a systematic approach for module configuration is an another critical obstacle making the design procedure more arduous and complicated. To ameliorate these knowledge and practice gaps, this study provides a new coupled generative adversarial network (CoGAN)-based framework for automated modular housing design generation. Furthermore, this approach encompasses a new module configuration algorithm that structurally modularize a generated housing design layout. This framework is expected to contribute to establishing the body of knowledge in a generative design of modular housing for mass building production and help architects and relevant stakeholders facilitate their design processes by yielding feasible, constructible, and optimal modular housing design alternatives.
Publication Source (Journal or Book title)
Automation in Construction
Recommended Citation
Ghannad, P., & Lee, Y. (2022). Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN). Automation in Construction, 139 https://doi.org/10.1016/j.autcon.2022.104234