Left unattended, these cracks can grow large enough to expose the underlying steel frame of the bridge itself. If the steel then rusts, the strength is compromised, and rebuilding is the only option. 'Concrete does not simply degrade overnight - it is a slow process. Therefore, being able to detect and predict potential points of damage in advance is extremely useful,' says Mikkel Hemmingsen, CEO of Sund & Bælt.
The end result in a process which increases safety while saving time and money. In addition, it has also allowed the company to carry its learnings over into newer bridge constructions. There are also plans to train the algorithm further, by applying the same method to the Little Belt Bridge, the Vejlefjord Bridge and the Øresund Bridge.
'Our core focus was to create a solution to maintain and improve efficiency, but we quickly realised that the more we used the solution, the better it actually became. This has given us an incentive to spread the solution to others,' Hemmingsen states.
From bridges to roads
Multinational construction company BAM Infra Nederland and OrangeNXT, a leading software integrator, have developed a system using Microsoft Azure, machine learning, and AI to train algorithms that can accurately detect and classify various types of damage on paved surfaces.
In the past, BAM sent out drivers in camera-equipped cars to capture photos and videos of road surfaces. Inspectors then reviewed the content to identify damaged areas, before marking them and making a plan to fix them. 'This process was time-consuming, costly, and tedious,' says Kitting Lee, Director of Commerce and Innovation at BAM Infra Nederland. 'We needed a smarter solution.'
The new solution sees vehicles equipped with 360-degree cameras record video footage from every angle, before they are uploaded to the Azure cloud, where AI-powered algorithms automatically flag any causes for concern. These images also capture geospatial data, allowing inspectors to accurately trace them back to their real-world location. This improves the speed, quality, efficiency, and accuracy of these visual road checks, enabling predictive asphalt maintenance, while reducing costs - freeing inspectors to focus their expertise where it is really needed.