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

Zhaoji Wu, Zhe Wang, Jack C.P. Cheng and Helen H.L. Kwok

Applied Energy, 2024, vol. 367, issue C, No S0306261924007013

Abstract: 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.

Keywords: Generative design; Knowledge graph; Sustainable building; Optimization; Building performance (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123318

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