A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN)
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.
Highlights A new deep learning-based method to automate modular housing design considering constraints of manufacturing and assembly A flexible automated module configuration approach compatible with the current practices in the volumetric modular housing Creation of 3D building information models of the generated housing designs to achieve higher design-production integration. Prototype development and validation of benefits by executing the integrated generative modular housing design system Establish a fundamental framework for developing context-based design generations using the AI technique.
Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN)
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.
Highlights A new deep learning-based method to automate modular housing design considering constraints of manufacturing and assembly A flexible automated module configuration approach compatible with the current practices in the volumetric modular housing Creation of 3D building information models of the generated housing designs to achieve higher design-production integration. Prototype development and validation of benefits by executing the integrated generative modular housing design system Establish a fundamental framework for developing context-based design generations using the AI technique.
Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN)
Ghannad, Pedram (author) / Lee, Yong-Cheol (author)
2022-03-27
Article (Journal)
Electronic Resource
English
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