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Sustainable Urban Environments

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To help make urban environments sustainable, one challenge is to reduce the waste produced from construction activities. Previous studies have identified that the global construction industry is responsible for approximately 40% of all carbon emissions and is the single largest consumer of materials on the planet. Of that material consumption, 10-20% will end up in landfills. Much of this waste, such as steel or concrete, is carbon intensive.

We aim to address the problem of waste in the construction industry – not in the sense that we use recycled materials to build buildings, but by investigating the following logic towards lean construction to achieve zero waste. However, there is one key question – who is responsible for the materials used and waste produced and where does the waste get produced? Is it the architect that produces it (after all they design the building), or is it the builder (after all they build the building)? In digital sustainability, we can take a step back and think about buildings, their designs and their materials as data we can use to investigate and assess what we can do with the data and if we can transform them into knowledge and tools that enable the construction sector to reduce its carbon emissions, material consumption and waste production.

We aim to apply a postdisciplinary, pragmatic approach working together with academics from business, the built environment, engineering (civil and manufacturing), computer science and data analytics. The overall agenda is to provide the foundation for a synthetic design method that combines machine learning and computational design to design sustainable and liveable cities. Below, we provide our proposed project list and key research areas.

Sustainable Urban Environments

CSS for 3D design

This project aims to build a 3D design framework as a ‘spine’ or ‘conveyor belt’ that allows a) data sourced in Project 1 to be

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Sustainable Urban Environments

Data mining/Data cleaning

This project aims to collect as much data suitable for the built environment as possible using web and HTML scraping, storing data in the REST

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