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Developing Prediction Model for Ground- Borne Noise and Vibration from High Speed Trains Running at Speeds in Excess of 300km/h
| Content Provider | Semantic Scholar |
|---|---|
| Author | Jurdic, Vincent Bewes, Oliver Guy Greer, Richard D. Marshall, Tom |
| Copyright Year | 2014 |
| Abstract | The UK Government believes that a new north-south rail link will revitalise Britain’s rail network. The Government has created HS2 Ltd, which is developing a Y-shaped high-speed railway to provide capacity and connectivity to populated urban areas at speeds up to 360km/h. A key concern for many is the environmental impact of the scheme, including the potential impact of ground-borne noise and vibration. The ground vibration generated by a train is expected to increase with speed hence methods for predicting ground vibrations at high speed are essential for a robust impact assessment. Currently there is limited published ground vibration data for trains travelling at speeds of more than 300km/h to validate existing ground-borne vibration models. However, there is a significant amount of data for trains travelling at lower speeds and the mechanisms that result in vibration generation at these speeds are well understood. This paper presents a model for predicting train vibration at speeds of up to 360km/h. The model is a development of an existing validated prediction model for ground vibration from the UK’s High Speed 1 railway which operates trains at speeds of up to 300km/h. This paper focuses on the part of the model that enables a measured train vibration spectrum to be scaled for train speed. The development has sought to improve the accuracy of the extrapolation to higher speeds by ensuring that the mechanisms that generate ground-borne vibration, such as wheel and rail roughness, are appropriate for the required speed range and by maximising the goodness of fit of the model with the available data at lower speeds. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://iiav.org/icsv21/content/papers/papers/full_paper_766_20140512143225248.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
