Currently the building design process is time consuming and labor intensive, I think there’s lots of potential for automating large swaths of it.
Kind of sounds like shape grammars [1] which can compose grammatical rules into designs and vice versa.
I think it would be interesting to use a shape grammar engine with Reinforcement learning, where the design is a state, action is a shape rule, and reward is some performance metric. The advantage over GANS (which seems to be the most common use of ML in architecture/design circles) is the integration of cumulative reward maximization better captures the way designs are incrementally built up.
One thing I've never seen is optimization techniques paired up with any sort of in-depth cost data. It's usually based on naïve extrapolations based on total material weight or volume, and doesn't take into account things like crane costs (a function of max component weight and building extents) local material availability, component fixed costs, number of connections, logistics costs, etc. There's a ton of low-hanging fruit in design optimization that you could pick before you even had to reach for AI-based methods, though the AI methods are obviously the long term future of the space.
[1] - http://www.math.wpi.edu/IQP/BVCalcHist/calc5.html#:~:text=A%....
That sounds like it might make the exercise more tractable. Without some sort of middleware, the article seems to be saying the researchers lack traction.
Or maybe the problem is a lack of data.
What if each building was a dataset that included digital versions of the client brief, the CAD files, the BOM and procurement folio, and a 5 year subjective client review? If there were millions of these datasets for training, the same learning could occur as with the shape grammar approach.
Perhaps the limitation today is not enough data, irrespective of data format.