The Learning Facade
1 online resource (39 pages) : PDF
University of North Carolina at Charlotte
Despite the rising popularity and utilization of intelligent systems, much of thebuilt environment, especially architecture, remains prescriptive or responsive in nature.Kinetic facades, especially, still rely on the analysis of historic or approximated data togenerate a solution through the utilization of a multi-objective optimization (MOO)algorithm. This approach lacks the ability to adapt to the changing forces (i.e. sitespecific micro-climates or changing occupants) to which buildings are subjected for tworeasons: 1) MOO is computationally expensive due to the immense solution space and 2)it can only solve for known objectives. This lack in ability for facades to adapt tochanging conditions or be designed using actual site data has been one of the hindranceson the growth of the industry.Kinetic facades should instead be developed as an integrated portion of anintelligent system that is able to unify environmental data and user input in real-time tocreate an interior environment that is comfortable, energy efficient, and able to adapt toany future changes. Inputs such as space volume and user preferences, then, must beassumed to be unknown, putting MOO at a disadvantage in intelligent systems. As such, Ihave developed a control system architecture for an intelligent facade that utilizes aneural network algorithm (a form of machine learning) to address the need of adaptationin kinetic facades. To test my method, I utilized Rhino 5, Grasshopper, and Python with asimulated dataset as a case study.
INTELLIGENT FACADESKINETIC FACADESMACHINE LEARNINGNEURAL NETWORK
Kim, Kyoung-HeeSauda, Eric
Thesis (M.Arch.)--University of North Carolina at Charlotte, 2018.
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