#Data-driven #GenerativeDesgin #C# #Grasshopper #DataVisualization
; generating building and plant structure
Office for Urbanization + Certain Measures | 2019
Supervisors: Charles Waldheim, Andrew Witt
Role: Machine Training, Data Visualization
Tools: Rhino, Grasshopper(C#), pix2pix
Publication: 50 New Town (in progress)
This work presents collaborative design research examining Chinese agrarian village block typologies,
augmented through computational processes and artificial intelligence (AI) classifications.
# Building Structure: How it works
For 18 building typology, from 18 different 10km x 10km region
- We trained the model to detect certain typologies using pix2pix.
We got the extracted boundaries of detected buildings matched with the trained typology.
We generated about 100 deep structures associated with each typology for training use.
We trained another model with the generated deep structure.
We got the generated deep structure from the extracted boundaries (from the 2nd process).
# Building Structure: Visualization
The generated deep structure elements were arranged according to their orientation and size.
# Plant Structure: How it works
The growth pattern became their language, and we interpreted 50 new towns with this logic. 3d tree models were generated based on their specific growth pattern.
With C# in Grasshopper, I built a 3D tree model generator that takes a few variables for the leaf shape and their attached pattern.
Tree growth logic
Grasshopper script with C# code
The growth patterns translated into computational language
# Plant Structure: 3d models
Examples of generated tree drawings
3d model of Jasmine in three different stages