The experimental validation of a numerical model for the prediction of railway induced vibrations

• Published in: Journal of sound and vibration Vol. 297; no. 3; pp. 512 - 535
• Main Authors: Lombaert, G., Degrande, G., Kogut, J., François, S.
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 06.11.2006; Elsevier
• Subjects: Applied sciences; Buildings. Public works; Dynamics; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Geotechnics; Invariants; Mathematical models; Physics; Railroad tracks; Soil (material); Soil mechanics. Rocks mechanics; Solid mechanics; Structural and continuum mechanics; Trains; Transfer functions; Vibration; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...); Invariant; Free field; Free vibration; High speed train; Rail vehicle; Frequency domain method; System identification; Experimental study; Transfer function; Modeling; Soil mechanics
• ISSN: 0022-460X; 1095-8568
• DOI: 10.1016/j.jsv.2006.03.048

This paper presents the experimental validation of a numerical model for the prediction of train induced vibrations. The model fully accounts for the dynamic interaction between the train, the track and the soil. The track geometry is assumed to be invariant with respect to the longitudinal direction, which allows for an efficient solution of the dynamic track–soil interaction problem in the frequency–wavenumber domain. The model is validated by means of several experiments that have been performed at the occasion of the homologation tests of the new HST track on the line L2 between Brussels and Köln. A first set of experiments is used to determine the dynamic soil and track characteristics. In a second set of experiments, the soil transfer functions, the track–soil transfer functions and the track and free field vibrations during the passage of a Thalys high speed train have been measured. These results are used for a step-wise validation of the numerical model that is based on the identified model parameters and allows to study the propagation of errors in the prediction model.