Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN)

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.