Generative Design for Buildings and Cities

David Benjamin

Generative design for architecture is a framework for combining digital computation and human creativity to achieve results that would not otherwise be possible. 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. This approach offers many benefits for designing buildings and cities – including managing complexity, optimizing for specific criteria, incorporating a large amount of input from past projects and current requests, navigating trade-offs based on real data, structuring discussion among stake-holders about design features and project objectives, offering transparency about project assumptions, and offering a “live model” for post-occupancy adaptation.

This presentation demonstrates the use of generative design in multiple projects at multiple scales. One case study is the design of a new office space for 300 people. Constraints included the size of the space, the number of meeting rooms, and fixed locations for cores and mechanical rooms. Goals involves qualitative aspects of human experience (such as work style preferences and adjacency preferences) as well as quantitative measures (such as daylight, productivity, and "buzz"). The space was designed and constructed in 2017, and it is now being monitored through sensors and surveys to fine-tune the space, to improve the scoring algorithms, and to be applied on future projects. Although this case involved an office space, the same generative design process can be applied to civic buildings, residential buildings, hotels, factories, schools, hospitals, urban plans, civil infrastructure, and mixed-use tall buildings.