Abstract
This paper outlines a vibration prediction tool, ScopeRail, capable of predicting in-door noise and vibration, within structures in close proximity to high speed railway lines. The tool is designed to rapidly predict vibration levels over large track distances, while using historical soil information to increase accuracy. Model results are compared to an alternative, commonly used, scoping model and it is found that ScopeRail offers higher accuracy predictions. This increased accuracy can potentially reduce the cost of vibration environmental impact assessments for new high speed rail lines.To develop the tool, a three-dimensional finite element model is first outlined capable of simulating vibration generation and propagation from high speed rail lines. A vast array of model permutations are computed to assess the effect of each input parameter on absolute ground vibration levels. These relations are analysed using a machine learning approach, resulting in a model that can instantly predict ground vibration levels in the presence of different train speeds and soil profiles. Then a collection of empirical factors are coupled with the model to allow for the prediction of structural vibration and in-door noise in buildings located near high speed lines. Additional factors are also used to enable the prediction of vibrations in the presence of abatement measures (e.g. ballast mats and floating slab tracks) and additional excitation mechanisms (e.g. wheelflats and switches/crossings).
Highlights
The rapid deployment of high speed rail (HSR) infrastructure has led to an increased number of properties and structures being located in close proximity to high speed rail lines (Carels, Ophalffens, & Vogiatzis, 2012)
A collection of empirical factors are coupled with the model to allow for the prediction of structural vibration and in-door noise in buildings located near high speed lines
A tool designed for the scoping assessment of in-door noise caused by high speed train passage was developed
Summary
The rapid deployment of high speed rail (HSR) infrastructure has led to an increased number of properties and structures being located in close proximity to high speed rail lines (Carels, Ophalffens, & Vogiatzis, 2012), Alternative frequency domain approaches have since been proposed by (Sheng, Jones, & Petyt, 1999), (Sheng, Jones, & Thompson, 2004) and (Konstantinos Vogiatzis, 2012) which used a combination of transfer functions for the train, track and soil to calculate vibration levels, and at large distances from the track. An alternative model based on the data collected in (Harris et al, 1996) was developed by (Federal Railroad Administration, 2012) to predict absolute vibrations from high speed rail lines This empirical approach used curve fitting techniques to develop relationships between train speed and distance from the track, with geological conditions largely ignored. The model uses a machine learning approach to approximate relationships for the effect of soil layering on vibration transmission These relationships are combined with empirical factors to facilitate rapid vibration prediction for a wide array of track and building characteristics. ScopeRail is compared to the performance of the original (Federal Railroad Administration, 2012) approach and it is found to offer enhanced performance
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