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The article doesn’t go into much detail, but it hints at the use of a technique that I’ve suspected might have some promise for building design - finding a way

The article doesn’t go into much detail, but it hints at the use of a technique that I’ve suspected might have some promise for building design - finding a way to convert a 2D set of drawings or a 3D model to a text-based representation, then using text-based AI tools like GPT-3 to generate new buildings based on input parameters. Currently the building design process is time consuming and labor intensive, I think there’s lots of potential for automating large swaths of it.

saeranv 6y ago
> finding a way to convert a 2D set of drawings or a 3D model to a text-based representation, then using text-based AI tools like GPT-3 to generate new buildings based on input parameters. 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. [1] https://en.wikipedia.org/wiki/Shape_grammar https://en.wikipedia.org/wiki/Shape_grammar
- briancp 6y ago
  This is really interesting, like a computer algebra system[1] but for geometry. Seems like it could be extended to cover the non-shape based documentation (material properties, design loads, etc) as well. 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%20Computer%20Algebra%20system%20is,sometimes%20difficult%20algebraic%20manipulation%20tasks http://www.math.wpi.edu/IQP/BVCalcHist/calc5.html#:~:text=A%....
- wombatmobile 6y ago
  > shape grammar engine with reinforcing engine. 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.

saeranv 6y ago

> finding a way to convert a 2D set of drawings or a 3D model to a text-based representation, then using text-based AI tools like GPT-3 to generate new buildings based on input parameters. 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. [1] https://en.wikipedia.org/wiki/Shape_grammar https://en.wikipedia.org/wiki/Shape_grammar

briancp 6y ago
This is really interesting, like a computer algebra system[1] but for geometry. Seems like it could be extended to cover the non-shape based documentation (material properties, design loads, etc) as well. 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%20Computer%20Algebra%20system%20is,sometimes%20difficult%20algebraic%20manipulation%20tasks http://www.math.wpi.edu/IQP/BVCalcHist/calc5.html#:~:text=A%....
wombatmobile 6y ago
> shape grammar engine with reinforcing engine. 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.