Date of Submission

Spring 5-5-2017

Degree Type

Thesis

Degree Name

Bachelor of Architecture

Department

Architecture

Primary 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.

Included in

Architecture Commons

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