Years before ZGF design technology specialist Dane Stokes embarked on a career in architecture, he was entrenched in the Las Vegas car scene.
At age 16, Stokes restored his first vehicle: a 1937 Plymouth. Eventually, he was named a lead designer and fabricator for a Porsche racing and restoration shop. He gained an appreciation for not only the design and construction of racing vehicles, but for the analytical approach to research and development: a blend of structural analysis, performance metrics and aesthetics.
Stokes noticed parallels between the automotive industry and architecture, where dual performance and aesthetic requirements pose complex challenges. He eventually returned to school to earn a masters in architecture from the University of Pennsylvania.
Today, his first career shapes his approach at ZGF. Stokes and colleagues regularly turn to computational design — tools for translating processing power into unique design solutions — to solve complexities inherent to high-performance architecture. He recently took some time to trace his career path, and to describe how computational design is being deployed on ZGF projects.
How does your background inform and inspire your work now at ZGF?
I have always had a love for building, designing, fabrication and problem solving. Although the entirety of my current work is centered around the power of the computer, it is really the thrill of making that drives me. Ultimately it doesn’t really matter what the end product is: a race car, a building, or a database. The process with which it is made, and the challenges that need to be overcome during the design process, is really where my passion lies.
Can you describe why you first started using computational design?
I started using it during my undergraduate studies. I was completing a series of fabrication projects including a wall of several hundred unique panel components. Adopting a computational design process allowed me to manage these unique items simultaneously, while building more complex and advanced installations. The ability to dictate design intent to an algorithm, and to have it respond with specific and intricate geometrical solutions, which are organized and quantifiable, was a real game-changer for my capabilities as a designer and builder.
How are you currently using computational design?
Computational design processes really excel where large amounts of data or objects in a design model need to be processed in unique ways. For example, the city of Seattle’s energy code requires architects to produce a drawing, wherein every unique façade panel is outlined and color coded based on its type. Areas of these panels are also calculated into a spreadsheet. A typical building of the scale we usually build has 10,000 to 20,000 panels on it. With computational design, we write an algorithm that shortens the time needed to complete a task from months into hours.
What’s the most interesting way you’ve applied computational design on a project?
We had an interiors project that involved weaving a couple hundred unique panels throughout a space, in unique positions and orientations. The act of making the panels wasn’t terribly difficult. However, when the sheets required to document the panels needed to be produced, the simple process of laying every panel flat and arranging them on sheets — with all dimensions and angles documented across these hundreds of panels — became totally unreasonable. The process was so labor-intensive that the designers were hesitant to explore multiple design options for the installation. In this example, writing an algorithm to automatically document every panel allowed the design team to focus on the actual design. This is how we use computational design: to allow more freedom for our design staff by focusing on the design decisions that really affect our finished product.
What’s on the horizon for future uses of computational design?
I see almost endless possibilities for the implementation of computational design within our industry. More advanced algorithms and more powerful computing systems will allow us to speak more generally to the computer programs we rely on to produce our work, rather than focusing on explicit commands.