unit-code
The negative impacts of e-waste is felt acutely around the world, with the amount of e-waste expected to reach 74 million tonnes by 2030. This will accumulate in low-income regions, mainly in Africa. This project explores new urban developments that tackle the global e-waste chain. The project combines urban growth and artificial intelligence, using machine learning to translate the urban environment into an architectural language. The design proposes a constant dialogue with machines to obtain multiple results rather than a single model, to investigate and find new urban possibilities that facilitate the recycling of e-waste.
The video consists of a digital animation showing the distribution of e-waste disposal around the world.
The video shows a detailed study of the e-waste recycling process, including a comparison between informal and formal recycling.
3D animation showing a detailed study of minerals found in a mobile phone.
Identification of the site project in London using the K-means clustering algorithm.
Digital animation of the new typology design research. The animation glitches the commercial space plan dataset with the recycling spaces.
The video shows a digital animation of 3D distribution growth and recycled material storage.
3D animation showing new machine landscapes resulting from the introduction of hinterlands into the architectural building.
3D animation of the building's growth and its visual impact on London's skyline.
3D animation introducing a machine-driven e-waste recycling system throughout London's high streets.