Stollwitzer, A., Bettinelli, L., & Fink, J. (2026). Approach for the redefinition of the damping factor of railway bridges. In EASD Procedia (pp. 1290–1293).
13th International Conference on Structural Dynamics, EURODYN 2026
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Event date:
27-Sep-2026 - 1-Oct-2026
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Event place:
Hannover, Germany
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Number of Pages:
4
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Peer reviewed:
Yes
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Keywords:
Railway Bridges; Damping; Bridge Dynamics; Structural Health Montoring
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Abstract:
Railway bridges are key components of the high-speed rail network, and their dynamic behaviour is crucial to ensuring safety and operational efficiency. With regard to damping behaviour, current standards specify conservative limit values and approaches that, in practice, often lead to uneconomical assessments of railway bridges. However, comparisons between measurements and standard values consistently show that the damping factors actually measured for bridges are significantly higher than the normative specification. This highlights the potential for a more realistic redefinition of damping factors. As part of a measurement campaign on 15 steel railway bridges in the existing Austrian railway network, natural freqencies and damping factors were determined using two excitation and evaluation methods: a forced vibration excitation using shakers and evaluation in the frequency domain, and an evaluation in the time domain based on decay processes after train crossings. The results demonstrate once again that the actual damping factors are significantly higher than those specified in standards and that the method in the frequency domain yields more reliable and reproducible results than the method in the time domain. Based on the data obtained, an alternative approach for defining the damping is presented, which takes into account additional system parameters such as the ratio of natural frequency to span, in addition to the span itself. This approach demonstrates the potential for a more realistic and less conservative redefinition of the damping factor in forthcoming guidelines.
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Research Areas:
Mathematical and Algorithmic Foundations: 33% Modeling and Simulation: 34% Computational Materials Science: 33%