What Bricks Want: Machine Learning and Iterative Ruin (2016)
article⁄What Bricks Want: Machine Learning and Iterative Ruin (2016)
abstract⁄Ruin has a bad name. Despite the obvious complications, failure provides a rich opportunityhow better to understand a building’s physicality than to watch it collapse This paper offers a novel method to exploit failure through physical simulation and iterative machine learning. Using technology traditionally relegated to special effects, we can now understand collapse on a granular level since modernday physics engines track objectobject collisions, they enable a close reading of the spatial preferences that underpin ruin. In the case of bricks, that preference is relatively simpleto fall. By idealizing bricks as rigid bodies, one can understand the effects of gravitational force on each individual brick in a masonry structure. These structures are sometimes able to ‘settle,’ resulting in a stable equilibrium state in many cases, it means that they will simply collapse. Analyzing ruin in this way is informative, to be sure, but it proves most useful when applied in series. The evolutionary solver described in this paper closely monitors the performance of constituent bricks and ensures that the most successful structures are emulated by later generations. The tool consists of two parts a user interface for design and the solver itself. Once the architect produces a potential design, the solver performs an evolutionary optimization after a few hundred iterations, the end result is a structurally sound version of the unstable original. It is hoped that this hybrid of topdown and bottomup design strategies offers an architecture that is ultimately strengthened by its contingencies.
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| Year |
2016 |
| Authors |
Harrison, Paul. |
| Issue |
ACADIA 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines |
| Pages |
72-77 |
| Library link |
N/A |
| Entry filename |
what-bricks-want |
