Author:
Yeo Siang Chuan,Lim Meng Hee,Eng Hoe Cheng
Abstract
This paper is about predicting the noise and vibrations for railways project in Malaysia by using Finite Element Analysis method. In recent years, Malaysia is massively developing railways industry. This has created a great awareness in the public, regarding noise and vibration from railways trains during operations are generated from the rolling interaction of the wheels with the rails. Additional noise may be generated from brake squeals during braking at the stations and curved segments of the rail alignments. Over the years, it is relatively easy to measure the acoustic sound power of a train and to calculate noise levels. However, it would be advantageous to be able to reliably and efficiently predict the noise and vibration impact resulting from proposed railways projects. Comprehensive noise and vibration shall be predicted to determine noise and vibration levels along the entire track alignment to ensure that noise and vibration levels shall comply with Malaysia Department of Environment (DOE) approved limits. The result of analyses can be used to identify and to design noise and vibration mitigation measures for the entire railway project. Several methods have evolved to predict noise and vibration from various operational sources, but their suitability for prediction of noise and vibration from railways trains is not well known and has not yet been thoroughly tested. This study document finite element analysis undertaken for the noise and vibration aspects of the viaduct design of the railways track. Data and inputs for finite element model and analysis are including the aspect of “Geometry, atmospheric, ground effect, analysis type and boundary conditions”, as mentioned by Makarewicz (1998) and Lamancusa (2009). A comparison of finite element model and analysis will be conducted by adding the additional aspects of “Material properties and applied loads”, which to be determined as better accuracy of predicting noise and vibration from railways train.
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