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.
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zhang et al. (2024) studied this question.
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