Group work / Academic
Location: Los Angeles, CA, US
Instructor: M. Casey Rehm
This is a new， compressed city; It's the size of a large complex building. According to our research, even in the densest neighborhoods of downtown Los Angeles, its density is extremely low compared to other cities. To radically increase LA's housing and population density, our project has targeted to inhabit the void spaces in the area.
In order to compress the master plan of the city to the scale of a large building, our team has reinterpreted the proportion of the city's zoning plan by first compressing the neural network. We collected many 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 the 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, generating a large number of functional cubes that are combined to become the final building massing.
Through 3D scanning, our team received many point cloud models with different functionalities. We replaced those color boxes by relating the previously obtained prototypes. We then build up six sets of connected boxes, each with its specific connection, to fabricate and examine these interesting relationships inside the complex building.