aggreg[AI]te-regener[AI]te



aggreg[AI]te-regener[AI]te focuses on creating an AI-driven process of building material reclamation and re-use. Using Detroit as a proving ground for developing this process, the project will investigate the growing number of home demolitions within the city and seek to provide new life for these vacant buildings, while also providing much needed re-densification of Detroit neighborhoods. The project will develop a speculative workflow which imagines how machine vision can be used to complete the multitude of necessary tasks from demolition to integration of recycled building materials. As a proof of concept for this workflow, we will test strategies for implementing specific tasks through AI neural networks, serving as preliminary versions of how machine vision can have future implementations.

Near the beginning of the 20th century, Detroit became the center of the automobile industry with a huge number of jobs available, attracting both large numbers of overseas immigrants as well as African Americans from the South. Detroit also became one of the prominent cities for wartime production during World War 2. This cemented Detroit as one of the largest and most prosperous cities in the country, ballooning the population to nearly 2 million residents by 1950. To support this large population and on the heels of high automobile ownership, thousands of single family detached homes were built, coming to nearly define 20th century suburban sprawl in the US.

In the past several decades, Detroit has gone through what can be described as an identity crisis resulting from social and racial strife and subsequent economic decline from plummeting population and increasing housing vacancy and dilapidation. Population has fallen from a peak of nearly 2 million residents in 1950 to just 670,000 in 2019. This has led to a huge amount of housing vacancy rate in the city, making it difficult and extremely inefficient to provide essential services when there are very few residents in a given area. Since 2014, the City of Detroit has used federal tax dollars to demolish more than 15,000 homes between 2014 and 2020 with a total project cost of $265 million dollars. There are still an estimated 22,000 blighted homes left to be demolished and, in 2020, voters passed a $250 million bond issue to demolish an additional 8,000 homes and renovate 8,000 others. The large effort to demolish older, abandoned homes to remove blight from Detroit’s neighborhoods is necessary for Detroit’s future, however this also has the effect of eliminating a large portion of Detroit architectural history and identity.

Our project is a recycling project from two standpoints – the first is recycling building material which can allow a more sustainable building future. Using machine vision to create novel ways to combine used materials into new components, this process can reduce demand for newly manufactured materials to supply our building needs. The second element our project seeks to recycle is architectural history itself. Through utilizing machine vision to synthesize Detroit home styles used commonly in the past, the process can give new life to Detroit’s identity and allow its past to continue informing its future. Recycling is the process of using specific separate elements and utilizing them coherently within a new form. This sense of recycling is through architectural features from separate home styles being utilized in a single synthesized Detroit-style home.  While this project focuses on the subject of Detroit, this can be considered only a single case study of the application of Artificial Intelligence’s possible future impacts on Architecture. The project itself begins to investigate how AI can enhance and preserve human history.
Faculty Advisor:
Matias del Campo