Date of Submission
Spring 5-5-2017
Degree Type
Undergraduate Thesis
Degree Name
Bachelor of Architecture
Department
Architecture
Committee Chair/First Advisor
Peter Pittman
Secondary Advisor
Ameen Farooq
Abstract
Kinetic architecture has traditionally been the domain of expensive, boutique projects which require a high degree of finance and engineering. This results in systems which are difficult to construct and maintain, and prevents widespread adoption of building technologies which offer adaptive possibilities to their users over time.
Additionally, such systems predominantly either respond to simplistic environmental models, or actuation for purposes of artistic effect. Neither approach primarily addresses the needs of the user -- an approach which architects should not accept.
This thesis shows that new technologies in analysis and construction techniques can be combined to create performative works which respond to the dynamic lives of their users. It proposes a machine learning model which can be trained on simulated occupancy conditions, and then drive an adaptive kinetic skin to conditions which meet user needs.