An interdisciplinary team of scientists from the UK are using artificial intelligence (AI) to design a self-healing form of asphalt that has the potential to save local councils millions of pounds each year on pothole repairs.
The material, made from biomass waste and designed using machine learning, is being developed by scientists from King’s College London, Swansea University and a research institution in Chile, and has been shown to fix cracks that form in it on its own without any maintenance or human intervention.
The team is also working with Google Cloud to run simulations regarding how this material’s behaviour changes under differing conditions, after one of the lead researchers on this work joined the Google Cloud Research Innovators Programme in 2022.
This programme is geared towards providing research teams with access to the technical resources needed to accelerate the pace of scientific discoveries. The work has enabled the researchers to shed some light on the reasons why bitumen, which is a major ingredient in asphalt, hardens and cracks through oxidation, and how this process can be reversed to essentially stitch the material back together.
“During the research, a type of AI known as machine learning was used to study organic molecules in complex fluids like bitumen,” said the group, in a statement. “The team developed a new data-driven model to accelerate atomistic simulations, advancing research into bitumen oxidation and crack formation.”
The self-healing properties in the asphalt are made possible by the addition of porous material known as spores that are smaller than a strand of hair and filled with recycled oils, which are released when the asphalt starts to fracture and can reverse the cracking process. “In laboratory experiments, this advanced asphalt material was shown to completely heal a ‘microcrack’ on its surface in less than an hour,” said the group.
According to data shared in the Annual local authority road maintenance survey report, compiled by the Asphalt Industry Alliance, potholes reportedly cost UK councils £143.5m a year to fix, with roads in England and Wales requiring an estimated £16.3bn of investment to fix in total.
Francisco Martin-Martinez, an expert in computational chemistry at King’s College London, said this work is geared towards helping asphalt “mimic the healing properties observed in nature” whereby wounded animals and trees heal over time as part of natural biological processes.
“Creating asphalt that can heal itself will increase the durability of roads and reduce the need for people to fill in potholes,” he said. “We are also using sustainable materials in our new asphalt, including biomass waste. This will reduce our dependence on petroleum and natural resources.
“Biomass waste is available locally and everywhere, and it is cheap. Producing infrastructure materials from local resources like waste reduces the dependence on petroleum availability, which helps those areas of the world that have limited access to petroleum-based asphalt.”
Jose Norambuena-Contreras, an expert in self-healing asphalt at Swansea University, said the research is pulling together experts in a diverse range of fields – including civil engineering, chemistry and computer science – to find a solution to the problem of potholes.
“By combining this knowledge with the state-of-the-art AI tools of Google Cloud, we aimed to enhance our understanding of bitumen’s healing capabilities through a bottom-up molecular design approach,” he said.
“We are proud to be advancing the development of self-healing asphalt using biomass waste and artificial intelligence. This approach positions our research at the forefront of sustainable infrastructure innovation, contributing to the development of net-zero roads with enhanced durability.”
Iain Burgess, UK and Ireland public sector leader at Google Cloud, added: “It is inspiring to see how teams at Swansea and King’s College London are unlocking the power of cloud-based and AI tools, including Gemini and Vertex AI, to drive more efficient processes and discover chemical properties.”
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