Scoping assessment of free-field vibrations due to railway traffic

The number of railway lines both operational and under construction is growing rapidly, leading to an increase in the number of buildings adversely affected by ground-borne vibration (e.g. shaking and indoor noise). Post-construction mitigation measures are expensive, thus driving the need for early stage prediction, during project planning/development phases. To achieve this, scoping models (i.e. desktop studies) are used to assess long stretches of track quickly, in absence of detailed design information. This paper presents a new, highly customisable scoping model, which can analyse the effect of detailed changes to train, track and soil on ground vibration levels. The methodology considers soil stiffness and the combination of both the dynamic and static forces generated due to train passage. It has low computational cost and can predict free-field vibration levels in accordance with the most common international standards. The model uses the direct stiffness method to compute the soil Green's function, and a novel two-and-a-half dimensional (2.5D) finite element strategy for train-track interaction. The soil Green's function is modulated using a neural network (NN) procedure to remove the need for the time consuming computation of track-soil coupling. This modulation factor combined with the new train-track approach results in a large reduction in computational time. The proposed model is validated by comparing track receptance, free-field mobility and soil vibration with both field experiments and a more comprehensive 2.5D combined finite element-boundary element (FEM-BEM) model. A sensitivity analysis is undertaken and it is shown that track type, soil properties and train speed have a dominant effect on ground vibration levels. Finally, the possibility of using average shear wave velocity introduced for seismic site response analysis to predict vibration levels is investigated and shown to be reasonable for certain smooth stratigraphy's.Ministerio de Economía y Competitividad - BIA2016-75042-C2-1-