Metropolitan Prosthesis
This thesis explores the transformation of East Manchester's urban landscape amidst the pressures of rapid urbanisation, population growth, mobility enhancements, and the introduction of future transportation. Adopting the lens of urban complexity and systems theory, we view the city as a self- organising system and extrapolate the future scenarios of East Manchester, in which we identify the spatial problems for this project. In 2073, the encroachment of vehicles and transportation infrastructures and dense urban fabric diminishes ground-level green spaces, hinders spatial accessibility for pedestrians, and limits sunlight access due to the overshadowing effect of high-rise buildings. The enhanced built areas exacerbate energy consumption and carbon emissions at the same time.
In response, our research integrates computational design methods with resilience and synergetics theories to envisage new urban configurations. We develop an adaptive mobility hub, a mixed-used building typology that facilitates vertical transitions and switches between different transport modes and doubles as a renewable energy station through emerging technologies. Centred on the mobility hub, we established generative elevated connection networks to the nearby buildings, creating new ground for pedestrians and cyclists. Our computational design methods involve generating these hubs and urban connections, iterating their distribution across elevated highway junctions, and evaluating various design options based on predefined spatial problem-solving criteria.
To effectively demonstrate our design outcomes, we intend to develop a user interface that displays various design iterations, each evaluated in scoring and analysis for key performance metrics such as carbon reduction, energy generation, green area increase, and pedestrian accessibility. We select the most promising options for each aspect and then communicate the advantages of the selections through detailed visualisations, including rendered scenes and videos. This approach provides an immersive experience, allowing users to fully understand our intentions of achieving a sustainable future and its potential impact.
