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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.