Quantification of Dynamic Track Stiffness Using Machine Learning

Quantification of Dynamic Track Stiffness Using Machine Learning

Railway track stiffness is an essential factor influencing the track conditions and long-term deterioration. However, the traditional ways to measure the track stiffness are based on inverse computations using multi-body simulations and/or finite element models, which are time-consuming and at low-s...

Full description

Bibliographic Details
Main Authors: Junhui Huang, Xiaojie Yin, Sakdirat Kaewunruen
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Track stiffness
axle box accelerations
dilated convolutional
machine learning
railway infrastructure
Online Access:https://ieeexplore.ieee.org/document/9830617/

Internet

https://ieeexplore.ieee.org/document/9830617/

Similar Items