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Group work / Academic
Location: Los Angeles, CA, US
Time: 02/2021-04/2021
Instructor: M. Casey Rehm

This is a new,compressed city; It’s the size of a large complex building.  According to our research, we found that even in the densest neighborhoods of downtown Los Angeles, its density is extremely low compared to that of other cities. In an attempt to radically increase the housing and population density of LA, our project has targeted to inhabit the void spaces in the area. 


In order to compress the master plan of the city in a scale of a large building, our team have reinterpreted the proportion of the city’s zoning plan by first compressing the neural network. We collected a large number of zoning maps and constructed the neural network through machine learning to visualize the functionality of each sector and how it has been composed. The floor plan of the building massing has input into the neural network for test to create a new floor plan of the designed building .


Our project used an algorithm to produce the final multifunctional building massing. The algorithm detects the color of our functional plane, and as a result  it generates a large number of functional cubes that are combined to become the final building massing.


Through 3D scanning, our team received a great number of point cloud models with different functionalities. We replaced those color boxes by relating the previously obtained prototypes. We then build up 6 sets of connected boxes, each with its own specific connection, to fabricate and examine these interesting relationships inside the complex building.

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