Innovation in Computational Design: Explorations, Insights, and the Benefits of Messiness
Espy Harper EDAC, SIX SIGMA GB Innovation Leader | Associate Principal
Espy Harper, LS3P’s
Innovation
Leader,
is a button pusher – in the best possible
way.
If she sees a new tool appear on a dashboard, she’s going to test it out and see whether it offers a creative solution (or just creates a hassle). We sat down with Espy to talk about the opportunities involved in computational design, how to use it to improve both design outcomes and data capture, and why it’s good to be out of our comfort zone now and then.
Let’s start with an intro-level question. What exactly is computational design?
Computational design is really just about using targeted algorithms to solve problems. Using programs like Dynamo and Grasshopper, we can write computer scripts that automate repetitive tasks. We can also create algorithms that allow us to set parameters and tweak variables for rapid iteration. We can propose a hypothesis and test it without investing days in sketching and modeling.
As LS3P’s Innovation Leader, how can you see this changing the ways in which we work?
The ability to explore multiple solutions quickly through a given computation is a game changer. For example, we can generate a variety of options to lay out corridors so that every point is visible from multiple areas for better visual control. Innovation is dependent on experimentation and for the designer, the time constraints on our work often limit our ability to experiment and look for correlations between design and outcomes. By quickly arriving at the most viable solutions, we save significant time—allowing us to focus more on refining the ideas that work. The goal is to achieve better, more innovative, and more effective
designs—any gains in efficiency are simply a bonus.
What can computational design do for us, beyond helping us to design better and faster?
Aside from the significant benefits of rapid iteration, computational design can help us look at things differently.
Do you remember creating Nolli maps in architecture school, where you view the city of Rome through the lens of building alignment pathways and vistas? Computational design is another tool to interpret and codify space, helping us to think about visibility or “integrated”-ness or other spatial characteristics in different ways. We can analyze spaces and really hone in and craft our experience of them. We can determine the computational rules that feed some of these experiences and adjust them in the design process.
Our work with computational design could be similar to the GPS guidance that UPS drivers use to eliminate nearly all left turns. The math shows that, even if turning left is the most direct route, left turns lead to more idling, which delays delivery times and burns more fuel. What kinds of “left turn” things are we doing in our work that could benefit from a different viewpoint, or a different look at the geometry? When we think about the scale of our work, small differences could have a huge impact.
Could we make a more compact floor plate, or a lighter structure, or a more energy-efficient building through computational design? And can we test each hypothesis in a matter of minutes instead of days, and then apply the savings to thousands (or millions) of square feet?
It sounds like it’s not just about keying in the right algorithms –there’s a science to this process for sure, but it’s also an art, right?
Absolutely. Like so much in our field of work, it’s left brain, and right brain, and the sweet spot between “wonder” and “rigor” that we talk about often. Think of the two-point perspective that we learned to draw in school. There’s absolutely a science to that process; you put your points down, and you draw your connecting lines, and then you know the proper angles to place your walls, and so forth. However, it’s also about art, and about creating a sense of three-dimensional space with character, depth, beauty, light – all of the things that make people engage with a space. The math creates structure, and the human element creates dimension and beauty. It’s kind of like where we are with AI, right? The results are only as good as the prompt we create. When we’re trying to answer a question or solve a problem through computational design,
it will take a fair amount of legwork to develop and refine a script that’s useful. The script or the algorithm can get you where you’re going faster once it’s built, but you also have to know where you want to go. Computational design doesn’t “compute” on its own; a human being creates and guides the process of building a tool.
It sounds like we might need to watch out for some pitfalls?
Well, no matter what tool we’re using in design, whether it’s a pen or a Dynamo script, we want to avoid being superficial. How we begin to push and pull and shape the “box” into something that nestles into its environment or meets the street or reduces carbon or makes life easier for the people who will work or live inside of it, goes deeper than aesthetics and needs to speak to the “why” of what we design. That said, you can be just as superficial with a pen. It’s always easier to build a box; of course it is. Either way, we want to avoid the trap of going straight from the project brief to “we need an X square foot building for X number of people for X number of dollars” without exploring the best (non-box) ways to achieve that goal.
What else should we consider?
I’m really interested in looking for opportunities to use some of these tools to aggregate data. What happens when we apply an algorithm across four or five buildings? What does that do? Does it give us any new insights about our work, our processes, our outcomes? We’re always working towards data-informed design and continuous improvement – can these tools help us in both of those areas? I think they can.
So – the possibilities here are really exciting, but even exciting change is hard, and people are busy and tired out there. Where would you suggest we start?
The great news is that LS3P already has a group of folks who are really good at computational design, and this group is growing. They’re developing test cases on how computational design can inform our work, and are finding ways in which these tools make our lives easier and our designs better. They’ve been operating kind of underground fitting this work in where it makes sense, but as we grow in our capabilities, we’ll have a wider and wider pool of talent. We don’t all have to be experts for computational design to have a significant impact, but we can all be aware of the opportunities.
We’re at a point of such rapid technological change that tool fatigue can be a real thing. It takes effort to figure out which tools are worth the learning curve, but it’s what we’ve always done as designers. Maybe we learned to iterate with a roll of trace paper, and that’s not going anywhere! These tools give us yet another way to think through design challenges and opportunities.
Look, change is messy! Innovation is messy. Research is messy. We’ll have moments of going down rabbit holes and coming back out again. When we throw ourselves into that uncomfortable space, just beyond our current skill set – that’s where the good stuff is. Nothing to be gained here in the shallow end; sometimes we have to wade out to where we’re paddling a little harder to understand what we can do. In that space, I think we’ll find the real human benefits of using computational design tools to elevate our work. We’re creating and shaping these tools, not the other way around. Stay tuned as we continue to explore and share!
Meet Espy
Since joining the firm in 2018, Innovation Leader | Associate Principal Espy Harper has consistently elevated our practice and our presence through her unwavering dedication and exceptional contributions. Her innovative spirit has brought fresh perspectives to our work, enabling us to tackle challenges head-on and seize new opportunities.
Espy is known for her history of energetic collaborative engagement, and she has consistently demonstrated a passion for working harmoniously with both internal and external teams on solving significant challenges. As Innovation Leader, Espy strives to leverage the unique skills that each team member brings to the table, harness diverse strengths, and build consensus by creating an environment where innovation thrives through collaboration. Her expansive vision encompasses technology, process, and design.
Over her 20+ year career, Espy has made significant contributions to the profession, earning the respect of her colleagues, clients, and the industry.