For the design of a new office and research space in the MaRS Innovation District of Toronto, we pushed the limits of generative design for architecture. We started with high-level goals and constraints and then used the power of computation to generate, evaluate, and evolve thousands of design options. The result is a high-performing and novel work environment for Autodesk that would not have been possible to create without this approach.
Generative design for architecture uses the same workflow as generative design for manufacturing, but it involves more complex goals and more stake-holders. We began our process by collecting data from employees and managers about work styles and location preferences. We then developed six primary and measurable goals: work style preference, adjacency preference, low distraction, interconnectivity, daylight, and views to the outside. We created a geometric system with multiple configurations of work neighborhoods, amenities, circulation, and even stacked private offices. Then we automated the process of exploring thousands of configurations and discovering ones that managed trade-offs and scored best.
This approach offers many benefits for designing office space—including managing complexity, optimizing for specific criteria, augmenting human creativity and intuition, 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 monitoring and restacking.