Rail corrugation occurring on track fitted with Egg fasteners has become a serious problem on newly operated lines of the Beijing metro in recent years. Investigations and field measurements show that the dominant wavelengths of corrugation are 63-100 mm, inducing dominant frequencies of vibration on the rail in the frequency range 160-310 Hz for train speeds 60-70 km/h. Due to the influence of the track dynamic behaviour on the formation of rail corrugation, various options for changing the dynamic behaviour of the track structure can be considered as possible solutions to treat the corrugation. Four treatment measures have been considered: rail grinding, adding a rubber pad within the fastener, replacing the fastener with a compression-type fastener, and installing rail dampers on the rail. Field experiments have been carried out to evaluate the effectiveness of these rail corrugation treatments on two lines of the Beijing metro. Frequency response functions (FRFs), track decay rates, and pass-by acceleration on the rail were measured, and the development of rail roughness was monitored. Experimental results show that the four measures are all effective to control or at least slow the growth of rail corrugation on tracks with Egg fastener. Rail grinding is mostly used to limit the effects of corrugation; here, it was noted that low-amplitude corrugation on some straight lines did not reappear after rail grinding. After adding a rubber pad within the fastener, existing corrugation reduced in amplitude, while corrugation did not reappear on newly ground rail. Replacing the fastener with a compression-type fastener and installing rail dampers on the rail were both found to slow the growth of corrugation.
Keeping railway tracks in good operational condition is one of the most important tasks for railway owners. As a result, railway companies have to conduct track inspections periodically, which is costly and time-consuming. Due to the rapid development in computer science, many prediction models using machine learning methods have been developed. It is possible to discover the degradation pattern and develop accurate prediction models. The paper reviews the existing prediction methods for railway track degradation, including traditional methods and prediction methods based on machine learning methods, including probabilistic methods, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Grey Model (GM). The advantages, shortage, and applicability of methods are discussed, and recommendations for further research are provided.
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