A rail service life prediction model based on BP neural network

Wanqiu Zhang; Qing Li; Jie Zhang; He Li

doi:10.1117/12.3015953

20 February 2024 A rail service life prediction model based on BP neural network

Wanqiu Zhang, Qing Li, Jie Zhang, He Li

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130643P (2024) https://doi.org/10.1117/12.3015953
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

The management of the rail service life is directly related to the safety of railway operations. This paper divides continuous rail line into 1 km grids, and uses the spatial and temporal data of rail damage, inspection and maintenance to identify and quantify the heterogeneous factors. Then BP neural network is used to construct a rail service life prediction model. Finally, the effectiveness of the model was verified using actual production data of ShenShuo Railway. The results show that the established model can accurately predict the rail service life, which has important guiding significance for the refined management of the rail condition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wanqiu Zhang, Qing Li, Jie Zhang, and He Li "A rail service life prediction model based on BP neural network", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130643P (20 February 2024); https://doi.org/10.1117/12.3015953

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available