Nowadays, there are multiple initiatives showing a renewed interest on railway transport of goods and passengers around the world. Thus, an efficient management of railway infrastructures, both at the operational level and in terms of economic profitability, is not only desirable but also corresponds to an area of ongoing research. In order to contribute to these efforts, an alternative and novel methodology to evaluate railway track support conditions is presented here, based on modal analysis of the characteristic frequencies of the multi-element system composed by a railway infrastructure and an instrumented vehicle moving over it. This methodology belongs to the group of vibration-based structural damage identification methods, and is focused on observing the characteristic frequencies of this multi-element system, which can be correlated with changes in the physical properties of the railway infrastructure under analysis. An important feature of the proposed methodology is that it should enable the collection of information regarding the conditions of the substructure of a railway infrastructure. By performing this assessment of a railway infrastructure over its length, and over time by comparing different rides over the same railway stretch, important information can be gathered regarding the support conditions of the track. This paper presents a complete description on the current stage of development of the proposed methodology, along with the theoretical model that serves as the basis to interpret the collected data. Preliminary verification of this methodology is performed through the analysis of two case studies regarding the passage of an instrumented vehicle over two underpasses. The results obtained so far show that the proposed methodology can provide relevant information regarding the support conditions of railway tracks.
Railway track support condition assessment—Initial developments on a vehicle-based monitoring solution through modal analysis
2022-10-01
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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