TY - CONF A1 - Auersch, Lutz T1 - Railway vibration – fast physics-based models for the prediction of ground vibration and the identification of track damage N2 - The following applications of machine learning will be discussed: 1. The prediction of the wave propagation from a railway line (completely physics based for surface lines, physics-based machine learning for tunnel lines) 2. The track behaviour for the emission of train-induced ground vibration (physics based for homogeneous soil, machine learning for layered soil) 3. Track damage detection and quantification from frequency response functions and moving load responses 4. Bridge damage detection and localisation from modal analysis and moving load 5. The use of axle-box acceleration for the identification of track/sub-soil condition and bridge resonances. The prediction of railway vibration usually needs time-consuming finite element, boundary element and wavenumber domain calculations. For a user-friendly prediction software however, fast calculations are needed. Several time-consuming detailed calculations have been used to develop simpler and fast models for the surface railway lines. The more challenging prediction from tunnel lines will be attacked by purely mathematical and by physics-informed machine learning. The dynamic stiffnesses of isolated or un-isolated railway tracks from detailed calculations with a continuous soil have been approximated with the simpler Winkler soil. The vehicle-track resonance (P2 resonance) rules the effect of the mitigation measures, and it can also be used for the on-board monitoring of the track and sub-soil condition. For the identification of track damage such as gaps between sleepers, track slabs and layers, detailed models with a continuous soil have been updated to get the best fit to the measured frequency response functions from hammer tests and the deformation pattern from the moving load response. Whereas the track damage can be locally identified, this is more difficult for bridges where the modal analysis gives mainly global information. The influence lines of the inclination for statically passing vehicles (locomotive, truck, compaction roller) have been used to localise bridge damage (stiffness variations). The on-board monitoring of rail bridges needs special conditions (regular trains with special speeds) to excite and measure the bridge resonance. T2 - 11th European Workshop on Structural Health Monitoring (EWSHM 2024) CY - Potsdam, Germany DA - 10.06.2024 KW - Vibration prediction KW - Track damage detection KW - Human and machine learning KW - Wave propagation KW - Surface line KW - Tunnel KW - Bridge resonance PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612462 DO - https://doi.org/10.58286/29865 SN - 1435-4934 SP - 1 EP - 9 PB - NDT.net CY - Kirchwald AN - OPUS4-61246 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Auersch, Lutz T1 - Railway vibration fast physics based models for the prediction of ground vibration and the identification of track damage N2 - The following applications of machine learning will be discussed: 1. The prediction of the wave propagation from a railway line (completely physics based for surface lines, physics-based machine learning for tunnel lines) 2. The track behaviour for the emission of train-induced ground vibration (physics based for homogeneous soil, machine learning for layered soil) 3. Track damage detection and quantification from frequency response functions and moving load responses 4. Bridge damage detection and localisation from modal analysis and moving load 5. The use of axle-box acceleration for the identification of track/sub-soil condition and bridge resonances. The prediction of railway vibration usually needs time-consuming finite element, boundary element and wavenumber domain calculations. For a user-friendly prediction software however, fast calculations are needed. Several time-consuming detailed calculations have been used to develop simpler and fast models for the surface railway lines. The more challenging prediction from tunnel lines will be attacked by purely mathematical and by physics-informed machine learning. The dynamic stiffnesses of isolated or un-isolated railway tracks from detailed calculations with a continuous soil have been approximated with the simpler Winkler soil. The vehicle-track resonance (P2 resonance) rules the effect of the mitigation measures, and it can also be used for the on-board monitoring of the track and sub-soil condition. For the identification of track damage such as gaps between sleepers, track slabs and layers, detailed models with a continuous soil have been updated to get the best fit to the measured frequency response functions from hammer tests and the deformation pattern from the moving load response. Whereas the track damage can be locally identified, this is more difficult for bridges where the modal analysis gives mainly global information. The influence lines of the inclination for statically passing vehicles (locomotive, truck, compaction roller) have been used to localise bridge damage (stiffness variations). The on-board monitoring of rail bridges needs special conditions (regular trains with special speeds) to excite and measure the bridge resonance. T2 - 11th European Workshop on Structural Health Monitoring (EWSHM) CY - Potsdam, Germany DA - 10.06.2024 KW - Vibration prediction KW - Track damage detection KW - Human and machine learning KW - Wave propagation KW - Surface line KW - Tunnel KW - Bridge resonance PY - 2024 AN - OPUS4-61231 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Auersch, Lutz T1 - Reduction in Train-Induced Vibrations—Calculations of Different Railway Lines and Mitigation Measures in the Transmission Path N2 - The reduction in train-induced ground vibrations by different railway lines and by mitigation measures in the propagation path was analysed in a unified approach by two-dimensional finite element calculations. In general, there was no reduction at low frequencies, and the reduction be-came stronger with increasing frequencies. A maximum reduction of 0.1 at high frequencies was established with an open trench. Reductions between 0.7 and 0.2 have been found for the other sit-uations, filled trenches, walls, plates, and blocks, as well as for railway lines on dams, in cuts and in a tunnel. Bridges can produce amplifications due to their resonance frequencies, but also strong reductions due to massive bridge piers. The influence of some parameters has been analysed, such as the bridge span, the inclination of the dam and the cut, the stiffness of the soil, and the tunnel structure. The dynamic track stiffnesses of a surface, bridge, and tunnel track have been calculated using the 3D finite-element boundary-element method for comparison with corresponding meas-urements. KW - Train-induced vibration KW - Mitigation KW - Trench KW - Obstacles KW - Tunnel KW - Bridge KW - Finite element KW - Boundary element PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-579573 DO - https://doi.org/10.3390/app13116706 VL - 13 IS - 11 SP - 1 EP - 19 PB - MDPI CY - Basel AN - OPUS4-57957 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -