Assessment of railway vibrations using an efficient scoping model

Abstract

Vibration assessments are required for new railroad lines to determine the effect of vibrations on local communities. Low accuracy assessments can significantly increase future project costs in the form of further detailed assessment or unexpected vibration abatement measures. This paper presents a new, high accuracy, initial assessment prediction tool for high speed lines. A key advantage of the new approach is that it is capable of including the effect of soil conditions in its calculation. This is novel because current scoping models ignore soil conditions, despite such characteristics being the most dominant factor in vibration propagation. The model also has zero run times thus allowing for the rapid assessment of vibration levels across rail networks. First, the development of the new tool is outlined. It is founded upon using a fully validated three dimensional finite element model to generate synthetic vibration records for a wide range of soil types. These records are analysed using a machine learning approach to map relationships between soil conditions, train speed and vibration levels. Its performance is tested through the prediction of two independent international vibration metrics on four European high speed lines and it is found to have high prediction accuracy. A key benefit from this increased prediction accuracy is that it potentially reduces the volume of detailed vibration analyses required for a new high speed train line. This avoids costly in-depth studies in the form of field experiments or large numerical models. Therefore the use of the new tool can result in cost savings.