Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network

Precast construction is a productivity-improving technology in the architectural, engineering, and construction industry that improves construction efficiency by combining factory-based manufacturing and lean assembly. Many international efforts have encouraged the adoption of this approach. This study presents an integrated Building Information Modelling (BIM) with technological automation interoperability to enable generative design and prefabrication for precast buildings. A generic BIM-based graph representation is established to explicitly formulate buildings' spatial and geometric features. Following this, a graph-constrained layout generator is developed, with a generative modelling algorithm and graph convolutional neural network, to extract pairwise spatial-geometric features for generating the optimal precast layout. This is followed by semantic enrichment of BIM data (i.e., Industry Foundation Classes) with precast data schema to facilitate data transformation for prefabrication automation until site delivery. The holistic approach presented in this study empowers pre-construction planning optimisation and fabrication automation in precast construction. •Formulate spatial-geometric features of precast buildings with graph representation.•Develop a graph-based convolutional neural network to predict building performance.•Develop a generative algorithm to automatically generate new building features.•Integrate GCNN and generative algorithms to optimise layouts of precast buildings.•Semantic enrichment of BIM data with PXML for automated prefabrication.