BIM-Integrated Generative Design for Sustainable Building

Abstract

This thesis explores how generative design, when integrated with Building Information Modeling (BIM), can support sustainable decision-making during the early stages of architectural design. Using Autodesk Revit and Dynamo as the primary tools, a custom generative workflow was developed and tested to create, evaluate, and optimize building forms based on solar radiation and spatial performance. The goal was to automate the exploration of design alternatives and help designers make more informed choices, both visually and numerically.

The study involved constructing a parametric model in Dynamo, embedding performance-based fitness functions, and exporting it to Revit’s generative design environment. Two generation methods—randomization and optimization—were compared to understand their impact on the results. Several solar analysis techniques were also examined, including Ladybug, Revit adaptive panels, and Dynamo-based nodes, to identify the most effective approach for early design analysis.

The research further investigated the underlying logic of Revit’s generative design system, including its use of evolutionary algorithms and fitness evaluation. By running multiple studies and filtering the outcomes, the workflow was used to identify high-performing design solutions, which were then translated into Revit geometry for further development.

This thesis demonstrates that generative design can significantly enhance the architectural design process by offering both creative freedom and measurable performance. While current tools have limitations, they already offer a strong foundation for sustainable, data-driven design exploration. The results point to exciting possibilities for future development in algorithmic design, multi-objective optimization, and deeper integration with environmental analysis tools.