Prediction of Rail Wear Under Different Railway Track Geometries Using Artificial Neural Networks

Prediction of Rail Wear Under Different Railway Track Geometries Using Artificial Neural Networks

The geometry of the railway track affects rail wear significantly. If the rail wear can be predicted and considered during the alignment design phase, the problems it causes can be mitigated at the source by optimizing the values and combinations of railway track geometry parameters. However, the re...

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Bibliographic Details
Main Authors: Hong Zhang, Weichen Shuai, Linya Liu, Pengfei Zhang, Kejun Zhang, Hongsong Lin, Yuke Zhang, Wei Li
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Infrastructures
Subjects:
rail wear
track geometry parameter
artificial neural network
particle swarm optimization
sobol sensitivity analysis
Online Access:https://www.mdpi.com/2412-3811/10/7/154
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