AIrBSound - Monitoring of transition structures on bridges

Initial situation of bridges in Germany

The transport infrastructure in Germany is under heavy strain and faces major challenges. The unplanned closure of bridges such as Rahmedetal Brücke (near Lüdenscheid), Salzbachtal Brücke (near Wiesbaden) and Ringbrücke at Magdeburg's Damaschkeplatz, the latter of which was recently classified as dilapidated, has highlighted the need for reliable infrastructure. Modernizing and maintaining bridges is therefore essential to ensure their functionality and availability, as they directly impact logistics and work routes, and thus indirectly impact the economy. This is especially important when considered in the context of the shortage of skilled personnel.

To make maintenance management of dilapidated bridges in Germany more intelligent, efficient and forward-looking, innovative solutions are needed to increase the safety and performance of traffic routes, detect bridge damage early and repair it promptly without lengthy full closures.

Brief explanation of the research project

The AIrBSound project is investigating the potential use of airborne sound sensors and microphones for “listening” to bridge damage caused by changes in condition. These technologies have not yet been used to monitor bridge condition. Initially, the focus is on expansion joint structures, which have a service life of around 25 years. As many bridges were built before this period, wear and damage to these structures is now to be increasingly expected.

Installing airborne sound monitoring systems in combination with AI-based analysis methods could continuously monitor the condition of roadway crossing structures and provide valuable information about traffic volume and type, helping us to better understand the traffic load on the bridge. If this new form of bridge monitoring proves successful, the acoustic wear detection system for expansion joints could be extended to other structural monitoring applications.

Innovation in bridge monitoring

The project's main objective is to investigate the potential of using airborne sound to detect anomalies in the transition structures of bridges. AI algorithms will analyse acoustic signals and classify various forms of degradation, such as soiling and wear, to enable efficient, economical long-term monitoring.

• Development of AI-supported methods for detecting state changes through airborne sound
• Preparation and processing of acquired audio data
• Use of established AI tools (e.g. neural networks)