On-Board Monitoring of In-Service Trains for the Structural Health Monitoring of Railway Bridges

Projektinformationen

OMoRail

ID Finanzhilfevereinbarung: 101104968

DOI

10.3030/101104968

EK-Unterschriftsdatum 11 Mai 2023

Startdatum 1 März 2024

Enddatum 28 Februar 2027

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 297 164,16

Koordiniert durch

POLITECNICO DI MILANO
Italy

Periodic Reporting for period 1 - OMoRail (On-Board Monitoring of In-Service Trains for the Structural Health Monitoring of Railway Bridges)

Berichtszeitraum: 2024-03-01 bis 2026-02-28

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Railway bridges represent critical components of the European transport network, enabling safe and efficient mobility of passengers and goods across the continent. Many bridge assets currently in service are ageing and require continuous monitoring in order to ensure safety, optimise maintenance costs, and support the transition towards more sustainable and resilient infrastructure systems. Conventional structural health monitoring systems are often expensive to install and maintain, which limits their large-scale deployment across extensive railway networks. Recent research has demonstrated the potential of so-called mobile monitoring approaches, where vibration measurements recorded on trains crossing a bridge are used to estimate the structural properties of the bridge without installing sensors directly on the structure. Such approaches could enable cost-efficient monitoring of a large number of bridges using existing railway vehicles, but several scientific and technical challenges still prevent their reliable use in practice. These include the influence of operational variability, environmental conditions, modelling uncertainties, and the difficulty of extracting structural information from indirect measurements.

The objective of this project is to develop advanced methodologies for the identification and monitoring of railway bridge dynamics using both direct measurements on the structure and indirect measurements obtained from passing trains. The project aims to establish robust data-driven and physics-based approaches to reliably estimate bridge modal properties, integrate monitoring data with numerical models, and quantify uncertainty in the identified parameters. The project pathway to impact focuses on enabling scalable and cost-efficient monitoring strategies for railway infrastructure. The expected results support infrastructure managers, engineering companies, and public authorities in improving maintenance planning, reducing life cycle costs, and increasing the safety and reliability of the European railway network. The project contributes to European priorities related to sustainable mobility, digitalisation of infrastructure, and resilience of transport systems.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

During the outgoing phase of the project, research activities focused on developing and validating methodologies for the dynamic identification and monitoring of railway bridges using vibration data from both on-board and structural sensors. A methodology was developed to estimate the natural frequencies of railway bridges based solely on vibration measurements recorded on trains crossing the bridge. The approach combines signal processing techniques, system identification methods, and numerical modelling to separate vehicle and bridge dynamics and extract structural information from indirect measurements. The method was validated using experimental data from real bridge crossings and demonstrated the capability to identify bridge modal properties without requiring instrumentation on the bridge.

In parallel, advanced direct monitoring approaches based on structural measurements were applied to bridge assets in order to validate the results obtained from mobile monitoring. Numerical models of bridge structures were developed and updated using experimental data, allowing the integration of measured responses with physics-based simulations. These activities enabled the comparison of different monitoring strategies and the assessment of their reliability under realistic operational conditions.

The project also investigated uncertainty quantification techniques for system identification, including statistical and subspace-based methods, in order to improve the robustness of modal parameter estimation under non-ideal excitation conditions. The developed methodologies were tested using both experimental measurements and numerical simulations.

The results obtained during the reporting period were disseminated through peer-reviewed journal publications, international conferences, invited talks, and special session organisation in scientific meetings. These activities contributed to the visibility of the project within the structural dynamics, structural health monitoring, and railway engineering communities.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The project advances the state of the art in structural health monitoring of railway bridges by providing validated methodologies for the identification of bridge dynamic properties using indirect measurements from operational trains. Existing monitoring approaches typically rely on permanent instrumentation installed on each structure, which limits their applicability to a small number of critical bridges. The methods developed in this project demonstrate that reliable modal identification can be achieved using measurements collected from regular railway traffic, enabling the possibility of large-scale monitoring without additional sensors on the bridge.

Another important advancement is the integration of indirect monitoring with direct structural measurements and numerical modelling. The project shows that combining mobile monitoring data with physics-based models allows a more accurate interpretation of measured signals and supports the development of digital representations of bridge behaviour. This contributes to the development of digital twin concepts for infrastructure systems.

The project also introduces improved uncertainty quantification techniques for modal identification under non-ideal excitation, which is a key challenge in real-world monitoring applications. The proposed approaches increase the reliability of identified parameters and provide information about the confidence of the results, which is essential for decision-making in infrastructure management.

The potential impact of these results includes more efficient maintenance planning, reduced monitoring costs, and improved safety of railway infrastructure. Further uptake of the results may require additional validation on different bridge types, demonstration in operational monitoring systems, and collaboration with infrastructure managers and industry partners. The methodologies developed in the project can support future research, standardisation activities, and the development of practical monitoring solutions for transport infrastructure.