A growing number of architectural design efforts are making use of spatial metrics that characterize the experience of people in built environments. Metrics can make qualitative experience-related factors quantitative, and thereby assist in the exploration of a parametric or generative design space. To facilitate the adoption and development of generative design workflows, we introduce a tool called SpaceAnalysis that performs pathfinding, visibility, and acoustics analyses from which a variety of metrics can be computed. A theoretical contribution arising from this work is a new discretization method that converts 2D building geometry into a grid-based data structure supporting all three types of analyses. Experimental results show that the new method accommodates narrow corridors and small doorways with an efficient grid resolution of about 25 cm. We apply SpaceAnalysis to recreate and make publicly available a generative design workflow that was previously used to lay out a 250-person office.
Buildings are the largest consumers of energy responsible for 48% of all Green House Gas (GHG) emissions. Due to the complexity and multidisciplinary aspects of architectural design, construction, urban design, and building occupant behavior, simulation has gained attention as a means of addressing this enormous challenge. The idea is to model a building’s many interacting subsystems, including its occupants, electrical equipment, and indoor and outdoor climate. With simulation results in hand, an architect is better able to predict the energy demand associated with various designs, and choose from among the more sustainable options.
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.