Computational design can identify greater efficiencies across the built environment, enabling us to create more sustainable cities.
Computational design and machine learning can help us to solve the most pressing issues facing our cities. Photo: Unsplash.
A new suite of design applications is in development at UNSW Sydney to help architects and urban planners optimise their designs for greater sustainability. The apps use machine learning to target the reduction of construction waste and urban heat, minimising the embedded carbon footprint of buildings.
According to lead researcher Associate Professor M. Hank Haeusler, Director of Computational Design at UNSW’s School of Built Environment, the tools will help minimise the environmental footprint of buildings by assisting built environment professionals in making more sustainable decisions around size, scale and materials.
“We’re applying a computational eye to these [today’s] global problems,” says the entrepreneur and designer. “Landfill, pollution, [the way different] materials [contribute to climate change], [as researchers] we have a moral responsibility to look into this.”
A/Prof. Haeusler works at the intersection of digital technologies, architecture and design. His expertise lies in computational design, including AI and machine learning, digital and robotic fabrication, virtual and augmented reality sensor technologies and smart cities.
In 2018-19, Australia generated an estimated 27 million tons of waste from the construction and demolition sector – 44% of the total national waste. The sector’s contribution to waste has grown by 32% per capita over the previous 13 years.
“The construction industry produces an enormous amount of waste. 10-15% of all the materials you bring onto a construction site are going straight into the bin,” says A/Prof. Haeusler.
“It’s wasteful, it’s bad for the environment, and it doesn’t align with the United Nations’ Sustainable Development Goals [that promote inclusive, safe, resilient and sustainable cities and environmentally responsible construction].”
Additionally, Australian cities are experiencing unprecedented levels of overheating. Urban overheating arises from human activity such as waste heat from industry, cars and cooling, building with heat-absorbing materials and rapid urbanisation, and adversely affects health, energy, and the economy.
Computational design and machine learning uplift our capacity to solve these global issues, A/Prof. Haeusler says.
“In a city, there are thousands and thousands of data sets. It’s like a jigsaw puzzle. Transport, urban design, economics modelling, urban heat, water, electricity – cities have super-complex systems.”
As humans, we might understand these issues in isolation. But machine learning helps us unpack the broader context and consequences of different design decisions, A/Prof. Haeusler says.
He says that machine learning can interrogate vast sets of fine-grain data in real-time to analyse and evaluate alternatives. In a design context, it can identify efficiencies and promote sustainable practices, in this case, reducing the heat and waste produced.
“[Within the UNSW heat reduction app,] you design your street and then a computer program does the calculation in the background [based on intelligence learned from its data sets. Then it tells you,] it looks like here, at this intersection, it will get hot because of the physics that shape urban heat islands.”
The designer can then adjust the building height, put in green spaces and shade, change the road width and adjust other variables to improve the building’s environmental footprint.
Similarly, the UNSW waste reduction app calculates the materials required for your design and allows you to adjust its size and scale to reduce waste offcuts.
