TY - CONF A1 - Auersch, Lutz T1 - Challenges of vibration prediction – realistic irregularities, the scattering of axle pulses, and the tunnel-surface reduction N2 - A prediction software has been developed by BAM. The following topics have still be solved. A realistic irregularity spectrum can be derived from axle-box measurements. It agrees wel with the spectrum used for the high-speed 2 project in the United Kingdom. In addition, the scattering of axle pulses should be included. This mid-frequency component can also be found in the HS2 procedure. Finally, the reduction in case of a tunnel line compared to a surface line should be included. Some measurement results of BAM, HS2 and other institutes show a certain mid-frequency reduction. This is due to the load distribution of the tunnel which yields softer axle pulses and the scattered axle impulses are reduced. T2 - ISO/TC 108/SC 2/WG 8 Working group Ground-borne noise and vibration from rail systems CY - London, UK DA - 21.11.2022 KW - Ground vibration KW - Railway trafiic KW - Prediction KW - Irregularities KW - Axle pulses KW - Tunnel line KW - Surface line PY - 2022 AN - OPUS4-56738 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Auersch, Lutz ED - Papadrakakis, M. ED - Fragiadakis, M. ED - Plevris, V. T1 - High-speed railway tracks of a surface, bridge and tunnel line and some effects on the train-induced bridge and ground vibrations T2 - COMPDYN 2011 - 3rd International thematic conference - Computational methods in structural dynamics and earthquake engineering CY - Corfu, Greece DA - 2011-05-25 KW - Track compliance KW - Surface line KW - Bridge track KW - Tunnel track KW - Ground vibration KW - Layered soil KW - Bridge resonance KW - Train speed KW - Axle sequence KW - Track irregularities PY - 2011 SP - 1 EP - 17(?) AN - OPUS4-24555 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 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 -