“It's a dangerous business, Frodo, going out your door. You step onto the road, and if you don't keep your feet,
there's no knowing where you might be swept off to.” ― J.R.R. Tolkien, The Lord of the Rings
Europe’s roads are the safest in the world. Current figures show that there are 50 fatalities per one million inhabitants, compared to the global figure of 174 deaths per million. Despite this, each loss remains a tragedy. In 2017, 25,300 people lost their lives on European roads.
The cause of these accidents can vary from human error and weather conditions, to damaged structures and surfaces. While some things are beyond the realms of control, road and bridge conditions are a variable which can be governed.
As soon as a road is paved, a combination of traffic and weather conditions begin to degrade and erode the surface. Undetected cracks, abrasions or defects can quickly lead to bigger problems, such as costly repairs, major traffic delays, and in the worst cases, unsafe condition. These problems are also shared by bridges, particularly when concrete is critical in maintaining the integrity of the structure. The earlier faults are detected, the sooner they can be addressed, saving time and money, while minimising disruption. Ultimately, this helps ensure that the roads themselves are safer for those travelling on them.
The detection of these faults, however, can be very difficult to carry out manually, especially as early-forming cracks are hard to spot with the naked eye. Predicting where faults are likely to occur ahead of time so that appropriate measures can be taken in advance also possess a massive challenge. Thankfully, technology is here to help.
Built more than 20 years ago, the Great Belt Bridge is a suspension bridge which connects the Danish islands of Zealand and Funen. Holding company Sund & Bælt, which is responsible for the maintenance of the bridge, has worked with Microsoft to deploy an innovative solution which combines the flexibility of drones, with the power of artificial intelligence (AI.)
The drones are used to fly around the bridge and capture thousands of pictures of the concrete structure – a method that’s far safer and faster than tasking a worker to dangle 200 metres above the surface to take pictures manually. The expertise and experience of these workers is instead used to help train a machine learning algorithm which can automatically detect cracks in the surface of the concrete, after the photos have been uploaded to Microsoft’s Azure cloud. After the AI creates a list of areas with cause for concern, the same experts are used to select the areas which need maintenance and repair.