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Project Discover: An application of generative design for architectural space planning
Danil Nagy, Damon Lau, John Locke, James Stoddart, Lorenzo Villaggi, Ray Wang, Dale Zhao, David Benjamin
Symposium on Simulation for Architecture and Urban Design
2017
Abstract
This paper describes a flexible workflow for generative design applied to architectural space planning. We describe this workflow through an application for the design of a new office space. First, we describe a computational design model that can create a variety of office layouts including locating all necessary programs and people using a small set of input parameters. We then describe six unique objectives that evaluate each layout based on architectural performance as well as worker-specific preferences. Finally, we show the use of a multi-objective genetic algorithm (MOGA) to search through the high-dimensional space of all possible designs, and describe several visualization tools that can help a designer to navigate through this design space and choose good designs. We conclude by discussing the future of such computational workflows in design and architecture. Our hope is that they go beyond basic automation to create an expanded role for the human designer and a more dynamic and collaborative interaction between computer design software and human designers in the future.
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Project Discover
Project Discover is a workflow for generative design for architecture. It involves the integration of a rule-based geometric system, a series of measurable goals, and a system for automatically generating, evaluating, and evolving a very large number of design options. The result is a tool to explore a wide design space, and get closer and closer to achieving all of the goals simultaneously.
