Title :
Assessment of railway performance by monitoring land subsidence
Author :
Ziyi Yang ; Schmid, Felix ; Roberts, Clive
Author_Institution :
Univ. of Birmingham, Birmingham, UK
Abstract :
Improvement of railway capability results in heavier axle loads and higher speed lines, which further induces railway subsidence. In order to ensure a good railway performance and reduce railway life cycle costs, railway subsidence should be measured regularly. The paper aims to assess railway performance by monitoring land subsidence along the railway, predicting railway subsidence in the future based on historical subsidence records. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is adopted in this research for monitoring land subsidence along the railway while Autoregression Moving Average (ARMA), artificial neural network and grey model are applied for subsidence prediction.
Keywords :
autoregressive moving average processes; axles; environmental monitoring (geophysics); grey systems; life cycle costing; neural nets; radar interferometry; railway industry; synthetic aperture radar; ARMA method; PS-InSAR; artificial neural network; autoregression moving average method; axle loads; grey model; land subsidence monitoring; persistent scatterer interferometric synthetic aperture radar; railway capability Improvement; railway life cycle cost reduction; railway performance; railway performance assessment; railway subsidence; speed lines; subsidence prediction; Assessment of railway performance; PS-InSAR; railwaysubsidence monitoring; subsidence prediction;
Conference_Titel :
Railway Condition Monitoring (RCM 2014), 6th IET Conference on
Print_ISBN :
978-1-84919-913-1