3D Graph Convolutional Neural Networks in Architecture Design

The nature of the architectural design process can be described along the lines of the following representational devices: the plan and the model. Plans can be considered one of the oldest methods to represent spatial and aesthetic information in an abstract, 2D space. However, to be used in the design process of 3D architectural solutions, these representations are inherently limited by the loss of rich information that occurs when compressing the three-dimensional world into a two-dimensional representation. During the first Digital Turn (Carpo 2013), the sheer amount and availability of models increased dramatically, as it became viable to create vast amounts of model variations to explore project alternatives among a much larger range of different physical and creative dimensions. 3D models show how the design object appears in real life, and can include a wider array of object information that is more easily understandable by nonexperts, as exemplified in techniques such as building information modeling and parametric modeling. Therefore, the ground condition of this paper considers that the inherent nature of architectural design and sensibility lies in the negotiation of 3D space coupled with the organization of voids and spatial components resulting in spatial sequences based on programmatic relationships, resulting in an assemblage (DeLanda 2016). These conditions constitute objects representing a material culture (the built environment) embedded in a symbolic and aesthetic culture (DeLanda 2016) that is created by the designer and captures their sensibilities.