A knowledge-informed optimization framework for performance-based generative design of sustainable buildings

Long computational time poses a significant obstacle to the practical utilization of performance-based generative design (PGD) in the early design stage. This study proposes a knowledge-informed PGD optimization framework for sustainable buildings, aimed to mitigate the time issue by integrating a knowledge graph (KG) into PGD. The first component of the framework is a PGD-KG schema that represents the topological relations within PGD. A generation method is then proposed for automatically developing PGD-KG models from parametric design models that are enhanced with semantic information. Furthermore, cross-domain reasoning algorithms are developed to enable automated compliance checking and performance evaluation based on regulatory requirements and sustainable design strategies, respectively. The proposed framework is applied to a design project focused on optimizing module layout to minimize cooling energy and maximize daylighting. The results demonstrate that the proposed framework can generate a satisfactory number of Pareto-optimal solutions while reducing computational time by 73.25% compared with the general optimization framework.