Areas of Activity
- Remote inspection of railway bridges supported by digital technologies and Artificial Intelligence.
- Onboard condition monitoring of railway infrastructures (bridges and track).
- Digital twins of railway bridges supported by IoT systems and data analytics.
- Innovative digital framework for condition assessment of railway bridges based on AI and data collected from SHM systems merged with data derived from computer-vision systems installed on UAVs.
- Development of a smart wayside monitoring system for safe railway transportation, identifying railway wheel defects and unbalanced loads.
- Estimate static loads for the dynamic weighing in motion of railway vehicles
- Development of predictive maintenance strategies and sustainability assessment methodologies for railway infrastructure, integrating monitoring, data analytics, and RAMS-based methodologies to improve reliability, availability, maintainability, and safety.
- Advanced fatigue assessment of existing steel bridges, combining multiscale numerical modelling and probabilistic approaches to support maintenance planning and life-extension decisions.
- Fatigue behaviour of metallic materials from low-cycle to gigacycle fatigue, integrating multiple fatigue assessment models and considering different material integrity states.
- Developments in standards and regulations governing the dynamic behaviour of railway bridges.
- Advanced train–bridge interaction modelling and numerical analysis for the assessment of bridge dynamic performance, train running safety and passenger comfort under operational conditions.
- Wheel–rail contact modelling within train–bridge dynamic interaction analyses, including its influence on vehicle response, bridge behaviour and overall system performance.
- Prediction, monitoring and mitigation of railway and construction-induced noise and vibrations.
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- Dynamics, performance and resilience of railway infrastructure systems.
Principal Investigator
Prof. Pedro Aires Montenegro (paires@fe.up.pt)