Intelligent identification of differential subgrade settlement of ballastless track system based on vehicle dynamic responses and 1D-CNN approach

Xu, Wenqian; Guo, Yu; You, Mingxi

doi:10.1016/j.trgeo.2024.101302

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Book1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Identification of Track Voids Caused by Differential Subgrade Settlement Through Integration of 1D-CNN and Vehicle-Track Coupled Dynamics

Zhang,

Guo

2024

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

Identification of Track Voids Caused by Differential Subgrade Settlement Through Integration of 1D-CNN and Vehicle-Track Coupled Dynamics

Zhang,

Guo

2024

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

A branched Fourier neural operator for efficient calculation of vehicle–track spatially coupled dynamics

Wang,

Sun,

et al. 2024

Computer aided Civil Eng

View full text Add to dashboard Cite

In railway transportation, the evaluation of track irregularities is an indispensable requirement to ensure the safety and comfort of railway vehicles. A promising approach is to directly use vehicle dynamic responses to assess the impact of track irregularities. However, the computational cost of obtaining the dynamic response of the vehicle body using dynamics simulation methods is large. To this end, this study proposes a physics‐informed neural operator framework for vehicle–track spatially coupled dynamics (PINO‐VTSCD) calculation, which can effectively acquire the vehicle dynamic response. The backbone structure of PINO‐VTSCD is established by the branched Fourier neural operator, which features one branch for outputting car body responses and the other branch for estimating the responses of bogie frames, wheelsets, and rails. The relative L2 loss (rLSE) of PINO‐VTSCD under the optimal hyperparameter combination is 4.96%, which is 57% lower than the convolutional neural network‐gated recurrent unit model. Evaluation cases from large‐scale simulations and real‐world track irregularities show that the proposed framework can achieve fast solution in scenarios such as different wavelength‐depth combinations and different wavelength ranges. Compared with the traditional vehicle–track coupled model, the speedup of the PINO‐VTSCD model is 32x. The improved computational efficiency of the proposed model can support many railway engineering tasks that require repetitive calculations.

show abstract

Intelligent identification of differential subgrade settlement of ballastless track system based on vehicle dynamic responses and 1D-CNN approach

Cited by 2 publications

References 19 publications

Identification of Track Voids Caused by Differential Subgrade Settlement Through Integration of 1D-CNN and Vehicle-Track Coupled Dynamics

Identification of Track Voids Caused by Differential Subgrade Settlement Through Integration of 1D-CNN and Vehicle-Track Coupled Dynamics

A branched Fourier neural operator for efficient calculation of vehicle–track spatially coupled dynamics

Contact Info

Product

Resources

About