Bridge Monitoring 

Europe's bridges, many constructed during the post-World War II era as part of reconstruction efforts, are now over 50 years old and facing challenges due to increased traffic volumes and heavier loads. This aging infrastructure necessitates continuous monitoring to ensure safety and structural integrity. Over time, degradation mechanisms and escalating load demands pose risks, making early damage detection crucial for maintenance planning and failure prevention. Vibration-based Structural Health Monitoring (SHM) leverages sensor networks to track the dynamic behavior of bridges, capturing changes that may indicate structural damage.  

Our Data-Driven Monitoring Strategy 

We leverage advanced technology and data analytics to enhance Structural Health Monitoring (SHM) for bridges, striving towards early detection of potential structural issues. By integrating cutting-edge machine learning techniques with vast datasets collected from densely instrumented bridges, our approach aims to improve accuracy and efficiency in damage detection. Our focus includes: 

• Understanding environmental and operational variability: Bridges experience constant changes in temperature, wind, humidity, and traffic loads. These variations must be accounted for, so damage detection remains accurate and reliable. 
• Machine learning for anomaly detection: We apply deep autoencoders and statistical anomaly detection to identify early deviations from normal behavior. 
• Automated data processing: Developing robust, scalable frameworks to analyze large datasets from continuous monitoring efficiently. 

What sets us apart 

• Real-World Data and Testing: We prioritize real-world validation by utilizing data from actively monitored bridges. Our access to a full-scale test bridge allows us to simulate and analyze structural damage in controlled conditions, ensuring our methods are both practical and reliable. 
• Advanced Multi-Technique Integration: Our approach combines AR models, modal parameter estimation, principal component analysis, autoencoders, etc. to enhance damage detection sensitivity and system reliability. This fusion of techniques enables comprehensive assessments. 

What's next 

• Enhance the generalization capabilities of unsupervised anomaly detection models. 
• Improve environmental compensation techniques to minimize false alarms. 
• Develop real-time, adaptive monitoring systems that dynamically adjust detection thresholds based on changing conditions. 
• Expand validation efforts with more diverse bridge datasets to ensure robustness across various structural types. 

Our work contributes to the long-term safety and sustainability of critical infrastructure by advancing state-of-the-art SHM methodologies and enabling proactive maintenance strategies.