Investigation of the forecast algorithm for assessing the risk of electrocorrosion in the lining of railway tunnels
https://doi.org/10.46684/2687-1033.2020.3.216-220
Abstract
Railway tunnels are more damaged by electrical corrosion from local conditions than other objects, since they contain a lot of moisture and emissions accumulated between the railway track and the rails. The analysis shows that the average service life of structures inside tunnels is shorter (on average by 40–50 %) than outside tunnels. A variant of intelligent systems for predicting electrocorrosion of tunnel structures is presented, a VR-neural network is applied to it, the advantages of which are currently recognized in different areas, including in the field of intelligent control. The study is devoted to an algorithm using a neural network of the backpropagation method to develop a system for assessing the risk of electrocorrosion in the lining of a railway tunnel
About the Authors
Ju Myong Jin
Pyongyang University of Railway Engineering
Korea, Democratic People's Republic of
Korea, Democratic People's Republic of
Ju Myong Jin — Ph.D., lecturer of the Department of Traction Electricity of the Electrical Engineering Faculty
Pyongyang, DPRK, Henjesan region, Hadan-1
Kim Kim Gwon
Pyongyang University of Railway Engineering
Korea, Democratic People's Republic of
Korea, Democratic People's Republic of
Kim Gwon — Ph.D., lecturer of the Department of Traction Electricity of the Electrical Engineering Faculty
Pyongyang, DPRK, Henjesan region, Hadan-1
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Review
For citations:
Myong Jin J., Kim Gwon K. Investigation of the forecast algorithm for assessing the risk of electrocorrosion in the lining of railway tunnels. Transport Technician: Education and Practice. 2020;1(3):216-220. (In Russ.) https://doi.org/10.46684/2687-1033.2020.3.216-220
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