Norway’s Most Remote Railway Bridges Are Becoming Smart

From offshore to railway

NGI has more than 50 years of experience with structural monitoring in demanding environments. The institute has, among other things, monitored offshore wind turbines, conducted distributed vibration monitoring in deep boreholes, and instrumented structures that are subjected to severe stress from salt, wind, and waves.

The technology NGI has chosen is based on so‑called magnetostrictive extensometers. Behind the name are sensors that measure linear displacement with extreme precision. The method has been a workhorse in the offshore sector for two decades, and will now be used to measure how the concrete in railway bridges stretches and compresses under load.

“Given the location and the difficult access, the equipment must be robust and of high quality. We have a three‑year warranty responsibility, and the system is expected to have a lifetime of ten years,” Heian explains.

Sensors on bridges and rails

NGI is responsible for instrumenting two bridges on the Ofoten Line. Nordal Bridge No. 1 is a supported prestressed concrete bridge with a single span of 50 metres. Bridge No. 2 has two spans of 44 and 41 metres and is located in a curve with a radius of 350 metres. Both bridges were built in 1988 and are representative of many railway bridges in Norway and Europe.

NGI’s solution includes several types of sensors that together provide a complete picture of the bridges’ condition. Extensometers measure whether the concrete is stretched or compressed under load. Accelerometers capture vibrations in three directions when trains pass. Inclinometers record whether the bridge girders tilt or lean. On the rails, strain gauges measure the forces from train wheels, while temperature sensors monitor how cold and heat affect the structure.

“The system solution is scalable and based on well‑tested, high‑quality components. This is important considering the location and the limited access to the site during the operational phase,” Heian explains.

Automatic detection of train passages

All measurement data are collected continuously at up to 1,000 measurements per second. The system is programmed to detect train passages automatically, based on signals from selected accelerometers. When a train passes, data is stored from 20 seconds before detection to 3 minutes after. Each train generates between 70 and 100 megabytes of data.

The data are transmitted via mobile broadband to NGI Live, the institute’s own cloud‑based data platform. There, users can set up alerts via SMS or email if operational status or measurement values deviate from normal levels. The data are visualised in real time through web‑based dashboards.

“NGI Live handles real‑time measurement data from thousands of sensors every day. It is a stable core component in our projects, meeting modern requirements for data security, access control, and user authentication,” Heian says.

Data for digital twins

All the data collected by the sensors serve a purpose beyond pure monitoring. A central objective of the instrumentation of the Søsterbekk bridges is to provide the basis for building digital twins.

A digital twin is a virtual copy of a physical structure, derived from sensor data and measurements. This digital model functions as a working tool: it allows engineers to simulate how the bridge responds to different loads, weather conditions, and stresses over time. Once the model is calibrated against real measurements from the structure, it becomes a reliable tool for understanding and predicting what will happen – and thus for initiating necessary maintenance before problems arise.

For the railway sector, the project represents a shift from periodic, scheduled maintenance to condition‑based maintenance. Instead of inspecting bridges at fixed intervals, monitoring data can tell maintenance managers when a bridge actually needs attention.

“Our highest priority is robustness and high‑quality measurement data that meet the user’s needs. If we achieve that, this could become an important reference project for how railway infrastructure will be monitored in the future,” concludes Kristoffer Heian.