A “Designer-centric” Framework for Building Massing optimization

Performance-based architectural design often faces challenges due to the complex interactions between wind, solar data, and building massing. Although massing optimization frameworks help address these challenges, limitations still exist, such as restricting designers' authorship and being time-consuming. To tackle these issues, a new workflow is proposed that combines wind and sun analysis, Evolutionary Algorithm (EA), and machine learning (ML) methods. This framework consists of two main stages: (1) combining designer intention with wind and solar simulations in an EA to generate optimized massing based on real-world buildings, and (2) training a machine learning model that remaps the distribution of the designer's intentions and building layouts. This study demonstrates the effectiveness of the proposed workflow in generating efficient and detailed building massing layouts that meet specific requirements. This "designer-centric" approach enables machines to optimize architect’s intentions, rather than forcing them to adapt to machine calculations. It will become an efficient, user-friendly human-machine collaborative tool for assisting in future building layout designs.