In the past decade,the rapid development of deep learning algorithms has enabled data-driven artificial intelligence in many fields outside the field of computer science,including generative design of architecture.A novel architectural design method of deep learning-based generative adversarial networks is proposed:the plan of the residential area within the boundary is automatically generated under the given boundary.The method refers to the design process of human architects,and its core is to learn the distribution characteristics of buildings and other elements from real plans through four closely related design steps,and finally generate new design proposals.In order to train the network efficiently,a dataset is prepared with the residential community design scheme as the original data.The generative systems allow for human modification during the design process,resulting in a co-design between humans and machines.After the generative model system is constructed,the feasibility and effectiveness of the generative model are verified by conducting generative design experiments in real-world projects.
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