Data-driven ballast layer degradation identification and maintenance decision based on track geometry irregularities-Reference-Cited by-同舟云学术

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Data-driven ballast layer degradation identification and maintenance decision based on track geometry irregularities

Published:2023-06-25 Issue: Volume: Page:1-23
ISSN:2324-8378
Container-title:International Journal of Rail Transportation
language:en
Short-container-title:International Journal of Rail Transportation

Author:

Zhao Wenbo¹,Qiang Weile¹,Yang Fei¹,Jing Guoqing²,Guo Yunlong³^ORCID

Affiliation:

1. Infrastructure Inspection Research Institute, China Academy of Railway Sciences Co., Ltd, Beijing, China

2. School of Civil Engineering, Beijing Jiaotong University, Beijing, China

3. Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands

Funder

Key Research and Development Project of the China Academy of Railway Sciences Corporation Limited

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Transportation,Automotive Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/23248378.2023.2228802

Reference47 articles.

1. Advanced Rail Geotechnology - Ballasted Track

2. Railway Geotechnics

3. Experimental and numerical study on lateral and longitudinal resistance of ballasted track with nailed sleeper

4. Ballast fouling inspection and quantification with ground penetrating radar (GPR)

5. Discrete element modelling of ballasted track deformation behaviour

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1. Correlation Analysis of Railway Track Alignment and Ballast Stiffness: Comparing Frequency-Based and Machine Learning Algorithms;Algorithms;2024-08-22

2. Advancing railway track health monitoring: Integrating GPR, InSAR and machine learning for enhanced asset management;Automation in Construction;2024-06

3. Ballast Settlement Accumulation in Zones with Unsupported Sleepers;Transportation Infrastructure Geotechnology;2024-04-15

4. Serviceability of railway slab track foundations with retaining walls under train loading;International Journal of Rail Transportation;2024-03-14

5. Data-driven clay-fouled ballast permeability assessment using analytical-numerical and machine learning approaches;Transportation Geotechnics;2023-11

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