Date of Award
Doctor of Philosophy (PhD)
Planning, Design, and the Built Environment
Michael Carlos Kleiss, PhD
Winifred Elysse Newman, PhD
Joseph Choma, PhD
Brandon Ross, PhD
With the increasing complexity of design problems in building performance, traditional design methods are difficult to meet the growing demand of designers. For example, in building facades with kinetic elements, traditional design methods are facing many constraints due to the complex design variables and requirements. This study applied reinforcement learning(RL) to building performance optimization and proposed a novel design methodology. This design methodology consists of two parts: (1) a reinforcement learning-based design system; (2) an improved design process based on reinforcement learning.
The construction of the design methodology started from the analysis of design algorithms. Next, the design system was built based on Python and Grasshopper. Then, this research proposed a design process that incorporates the reinforcement learning algorithm. Finally, a full factorial experiment was conducted to verify the generalization and effectiveness of this design method in different scenarios. Results of the experiment showed that kinetic facades generated by the novel design method perform better than facades generated by traditional design methods in terms of blocking radiation heat and glare.
The application of reinforcement learning in architecture is still in the exploratory stage and has many unexplored research directions. By proposing a feasible and efficient reinforcement learning-based design methodology, this study will improve the performance of buildings and provide references for applying reinforcement learning in design.
Dai, Sida, "Reinforcement Learning Based Design Methodology for Building Performance: A Case of Building Facades with Kinetic Elements" (2021). All Dissertations. 2928.