Portfolio
Academic
This page details work across my academic career, starting with my most recent thesis on the role of generative design methods in responding to the COVID-19 pandemic.
Archisearch 'Pandemic Architecture' Submission
Thesis Introduction
The COVID-19 crisis has shown that governments need to prepare responsive physical systems prior to the occurrence of future infectious diseases. In this context, I explored the question - how can a generative design method be used to configure an emergency hospital system that can be deployed during an epidemic, which simultaneously isolates and is comfortable for patients?
Our project AID, Architecture for Infectious Diseases, is both the construction and generative design system. This means AID can be deployed effectively on any site in the UK, whilst maximising the potential for human comfort. AID is designed using standardised dimensions, so that the primary structure can be repurposed after an epidemic.
Programmatically the focus of AID is on the Isolation and ICU modules, as well as the parallel contaminated and clean circulation systems. Isolation modules accommodate patients who have, or are expected to have, the virus. Intensive care unit (ICU) modules facilitate two patients who require the use of ventilators. Both the isolation and ICU modules have dedicated air handling systems which maintain negative air pressure, as well as filtering contaminated air.
Generative design has been used to configure the system on a site in Manchester. This is the process of using algorithms to iterate through designs by the definition of key design goals, which are parameterised and varied to create iterations and consequent analysis. For this project, the design goals were focused on the potential for patient and staff comfort, which we have linked to sunlight and views, and generated 1200 iterations. Once selected, we added to the design by: manually organising the ground floor programme, allocating ICU and isolation modules to cells, and by adding communal spaces. For details on the process used generate, measure and compare iterations, see the portfolio below.
AID is an example of a creative and thorough construction system, suited to biocontainment, and an innovative use of generative design to quickly react to emergency situations.
Thesis 1
An Introduction into
Generative Design
Serving as an introduction into computational ways of thinking and working. We were tasked in our first unit to generatively design a serpentine pavilion. This project was a springboard for main thesis project: AID (Architecture for Infection Diseases). It involved a dive into theoretical thinking behind computational methods and a hands-on learning of the tools available to us - mainly Grasshopper.
Thesis 2
COVID-19
As the threat of COVID-19 grew at the beginning of the year, we decided to use the opportunity of our thesis project to question what the event of an architectural response to a global pandemic would look like. This unit was an exploration into different types of responses and considerations that would have to be made before arriving at a proposition in Thesis 3. Using our knowledge gained from Thesis 1 we began to outline some key parameters moving forward. The full submission can be seen to the right.
Thesis 3
Architecture for Infectious Diseases (AID)
Thesis 3 focused on leveraging the skills learnt across Theses 1 and 2 to develop a proposal for an architectural response to the COVID-19 crisis. Further details can be found at the top of this page and in the portfolio to the right.
Professional Studies
As part of the 2019/2020 Professional Studies unit at Manchester School of Architecture, we were tasked in groups of 12 to design a hypothetical architecture practice. We were encouraged to anticipate the change in the professional environment as technology and society develops. My role focused on looking towards the use of emerging technologies, contributing to the virtual technologies section of the design proposal and the video on the left. The full design documents can be found below.
PREVIOUS YEARS
BA_Year 3
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BA_Year 1