Thesis | ITP | Advisor: Nancy Hechinger
Self-assembly occurs everywhere in nature. Can we use the principles of self-assembly as a design method to generate architectural forms? And would this approach enable us to better respond to environmental challenges in the future?
Think about a system of building blocks, a kind of LEGO set for nature, that follows the logic of physical phenomena, responds to its environment, and reconﬁgures itself until it reaches a stable pattern. Such a system could be used to inform design decisions by revealing the natural forces that might aﬀect architecture in a speciﬁc environment.
Current self-assembly research seeks to design a shape in advance (deterministic self-assembly). This project embraces randomness and chance, and takes advantage of what would otherwise be considered errors (non-deterministic self-assembly). I imagine a future where using self-assembly design methods, we will be able to develop an architecture that draws on and is nurtured by the logic of nature.
Now think about a system that can model a series of shapes that emerge from a stochastic environment. Shape-finding algorithms generating adapted architectures that respond to the particular conditions of a specific environment. We could even imagine a future where architectural projects are designed using similar methodological approaches to assess risk factors, for example.
We could even speculate about better understanding a specific environment by analyzing the resulting series of shapes using machine learning models to discover common characteristics shared by the most notable patterns.
For my experiments, I had to design an environment. A building of inverted swimming pools that hold air instead of water, immersed into 20gl fishing tank, and two hydro-jets.
Systems such as this one could be used to inform design decisions by revealing the natural forces that affect architecture in its environment. Deterministic self-assembly–previous video–seeks to create a shape in advance, while non-deterministic self-assembly–next video–allows the system to find shapes that better adapt to its environment.
I envision a future where self-assembly design methods, allow us to develop an architecture that draws on and is nurtured by the logic of nature.
After the assembling process is completed, we can 3D scan the resulting architecture to obtain the shapes that emerge from a specific environment.
Snowflakes are structures that grow in symmetric fractal patterns.
Can we design modular self-assembling system that grow in fractal patterns?
Can these systems grow outside the lab?
Can self-assembly be guided by self-similarity? (The Koch snowflake case)
#physical computing #computational design #analog algorithms #generative design #programmable matter #fractal growing #self-assembly #self-organization #autopoiesis