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The road to net zero: faster with AI?



Artificial intelligence (AI) is already an everyday part of our lives, whether through social media, online shopping or simply a search on Google. But the last couple of years have seen a meteoric rise in its use, with forecasters predicting that trend to continue over the next decade.


Perhaps one of the most promising outcomes that our deployment of AI could bring is a faster journey to net zero carbon. While it’s already clear that data is vital in recording and measuring progress in cutting carbon – from our own activities and from all the organisations that feed into our activities - the ability to analyse and interrogate that data more quickly and thoroughly will surely lead to better decisions and new ways of doing things.


Researchers are already looking at how to use AI to optimise asphalt mixes. While Swiss engineers G+P say that they can use AI to check the acoustic performance of different mixes.


AI in action

Perhaps the most obvious way that AI can help reduce carbon emissions is by improving efficiency. Treating cracks in a road before they become potholes, for instance, saves significant amounts of carbon.


Some AI tools already aim to boost efficiency. Contractor Keily Group has started using a platform called 3DAI City, a system designed ultimately to be deployed on city vehicles and take note of everything from parking spaces to congestion to crime. In this case, Keily says it will be using the technology to spot cracks and other surface deteriorations so that they can be treated before they worsen to become potholes.


Meanwhile, West Berkshire Council has become the latest authority to deploy Vaisala. This system can use smart phones to capture digital images of the road condition, traffic signs and road markings, using AI to recognise them. Authorities can gather data more frequently, so that the condition of its roads are better mapped out and maintenance and interventions can be better planned.


Taking the next step on, AI can be used to optimise the way that a whole programme of maintenance and repairs is organised. We are already seeing tools like this deployed for the programming of major infrastructure projects. For example, several projects on HS2 have deployed ALICE Technologies which takes information from a BIM model, together with resourcing information, and then offers multiple programming options. Contractors report that it can save significant amounts of time – and money.


Beyond that, there is the smart city vision of automated repairs: robots being directed to carry out the repairs and maintenance that have been scheduled by the AI.


Green energy

In parallel with improving efficiency to cut carbon is our need to transition away from fossil fuels. Renewable or green energy undoubtedly is central to that, whether it’s being used to charge electrical vehicles and plant or to split water to create green hydrogen.


But how are we going to make sure that there is enough renewable energy to go round all the imagined uses? There are all sorts of problems to overcome, from how to store electricity produced from sources such as solar or wind to how to upgrade power networks so that electricity can be distributed to all the places it is going to be needed.


Here, again, AI will help. It can be deployed to better forecast what demand will be and then automatically operate infrastructure so that it starts producing power. It can also be useful for managing small-scale electricity networks that supply small communities or rural locations.


Future vision

Ultimately, could all these different uses of AI somehow join up, so that everything is optimised with data from different parts of the process from design to manufacture to road laying to maintenance are all joined up? Well, very possibly, although that is some way off.


In the short-term, we are promised that AI will boost productivity and value. With tools and services already out there, now could be a good time to start

 

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Thermal Road Repairs: Decarbonising the asphalt repair industry


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