FOOD FOREST

2022 Summer Status Area Instructor

SCI-Arc Completed

9,940 sqm

Michael Casey Rehm

Soomeen Hahm

John Cooper

Design Development|

AI: CycleGan, Style Transfer

Generative design: Processing,Houdini,Zbrush,Rhino,Grasshopper,Python

Solar analysis: Grasshopper Ladybug

Visualization : Unreal Engine 5

Presentation: Adobe suits

Food Forest is an innovative design approach that offers a comprehensive solution to the challenges of food insecurity and housing inefficiencies in urban areas. By combining living spaces with markets and vertical hydroponics farming, it provides an automated formula for generating housing proposals that respond to local climate and economic conditions.

Using advanced technologies such as 3D and 2D

Generative Adversarial Networks (GANs), as well as voxel and component-based procedural algorithms, Food Forest translates the results of solar radiation simulations and economic/energy calculations into the formal qualities of the space. This enables the integration of vertical farming and market spaces into the design, resulting in a sustainable and functional environment that caters to the needs of the community.

Issue

Homelessness is a global challenge. About 100 billion people have no housing in the world.

A key focus of this design formula is to use AI to generate more efficient housing designs, reduce pressure on food supply, and achieve economic, environmental, and social sustainability.

I choose three metropolitan to test this design formula and a deep-developed LA site as my example. Vertical farming and market spaces into the design, resulting in a sustainable and functional environment that caters to the needs of the community.

Program

This system also provides fresh local food through vertical hydroponics farming, which efficiently grows plants and fish simultaneously and is capable of providing people with carbohydrates, protein, and vitamins.

I aim to create a module-based mixed-use residential building that represents historical public architecture section moments by bringing solar radiation data into a neural network and comparing and extracting interesting parts of the model as my components.