As an architect, when was the last time you had a night off or a whole weekend to sit by the pool sipping a cold beer and watching your kids play?? I suppose you can hardly recall. For many architects, working nights and weekends is part of the job, especially when project deadlines loom. Work-life balance does not exist in their vocabulary. As a matter of fact, architects don’t feel empowered to ask employers for work-life balance benefits like working remotely and having flexible hours.
Before I go on writing this piece, I know you might be asking yourself, what is this work-life balance word I keep throwing around? Well, experts define work-life balance as, “the equilibrium between an individual’s priorities at work and their priorities in other aspects of life.” But in most cases, it is hard to define it in a way that makes it applicable to every individual in the same manner. Let’s be honest, what suits you in terms of work-life balance, may not work for your family, friends, staff, etc. right?
So, for the future of architecture, automation and machine learning (ML) show promise in alleviating some of the pain I mentioned above. These innovations are taking over menial tasks and giving architects more time for design creativity (and anything else they’d rather be doing).
In the past few years, researchers have been putting ML to the test for speculative architecture-design projects. We are seeing three ways that ML-assisted design has the potential to augment architects’ skills, improve productivity, and automate drudgery.
- Design automation or Generative Design
This is where the designer inputs constraints or parameters, and the algorithm creates design options.
- Design insight
This is where the architect fully controls the design, but ML provides insight and suggestions on matters such as local building-code requirements. This gives architects more freedom to design, with helpful (but hands-off) guidance that can speed up their workflow from planning to pre-construction.
- Design interaction
This is where the ML software is co-creating the design with the architect and automating the more menial parts of the work.
Let me break it down how Machine Learning will help improve the work-life balance of architectures.
FROM BIM TO GAN
We have tried testing ML’s feasibility in the architect’s workflow to demonstrate a scenario where humans and machines work together.
To keep the experiment relatively simple, let us choose part of the third floor rather than the whole building as a test bed. Let’s focus on three types of components—cubicles, conference rooms, and phone booths—agreeing that those would provide enough information to provide a compelling proof of concept.
We collected all variations of these three components used in their current projects and created a data set that contains all of the possible spatial combinations of each type.
An ML model “learns” by finding patterns in a large data set—in this case, interior-layout examples for an office building. One of the basic principles in ML is that the model must be trained on both good data and bad data—data that tells the model which outcomes you want (practical, pleasant, productive working environments) and which you don’t want. If we gave it only good data, it wouldn’t know when it did something wrong; it might have cubicles overrun walls or not allow enough walking space in between.
For this project, we choose a specific type of model called generative adversarial network (GAN). Like a human designer, a ML system can quicken and deepen its grasp of a domain of knowledge by repeatedly challenging its assumptions about what it’s already learned.
In a GAN, there are two models challenging each other. Both are trained to “know” what good office layouts look like: combinations of furniture, infrastructure (such as HVAC and plumbing), light, and space that represent good office design. One model is constantly generating combinations of these features and challenging the other model to accurately label it a “good” or “bad” design.
If the Test Fits . . .
To make the BIM-to-GAN process real, one can create a sketch interface that follows a workflow called “test fitting”: The architect selects part of a commercial office building and tries to optimally fit in the target number and ideal configuration of phone booths, conference rooms, and cubicles into the chosen space.
Using a pen and tablet, designers can draw the outline of the area to consider. Then they can enter the target data describing what to fit into the space. The geometry of that space could then be converted into BIM, rendering the sketch as full-fidelity 3D geometry.
A major challenge for architects is when they need to process changes to an office layout. For example, 400 cubicles instead of 350. Those changes typically require the designer to start from scratch in Autodesk Revit, which can be a painstaking process to create and compare variations of a space. Without ML technology, architects generally cram in everything requested by their client, and in the end, they may not be able to hit the exact number of project targets.
But when the GAN allows the exploration of alternative layouts, it saves a huge amount of drudgery for designers and frees them to focus on the creative elements of their work. Sketching might take 90 seconds, but that 90 seconds of processing can represent two to three weeks of back-and-forth work for designers at Gensler working in Revit without ML assistance.
The model also enables users to convert the design directly into a virtual-reality (VR) interface. Two minutes after you sketch, you could put on a VR headset and walk around in the space you created to experience what that office environment would actually feel like. If you didn’t like it, you could spend five minutes and come up with alternative approach.
Room designs can be viewed and modified within a VR interface. Courtesy of Chin-Yi Cheng.
The Future of Workflow
While ML may one day automate some of architects’ mundane tasks, the goal should be for designers to maintain control of the creative aspects of design. An architect uses uniquely human skills to create an environment that’s a delightful place to work and interact.
It will be difficult to teach GANs intangible parts of design, such as fit and finish, materials aesthetics, and how well the surroundings support collaboration among teams. But there is an important role for ML in computing an optimal layout allocating space for different types of activities and determining building-infrastructure needs. The technology could provide guidance to help architects choose the most practical and constructible design.
Doesn’t that sound like a weight could be lifted off of your shoulders? It may take a few years before ML-assisted design is widely available to architects. And it’s difficult to quantify our examples’ impact on productivity because it wasn’t an optimized, commercial release of a tool. But it did show that we can apply ML to a workflow that increases productivity—and decreases tedium—for architects, engineers, and builders. One day, that might allow architects to take an extra vacation once in a while.
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