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2025 | OriginalPaper | Chapter

AI-Based Ballasted Track GPR Application and Development

Authors : Xuanyang Qin, Zhan Peng, Guoqing Jing

Published in: Recent Advances and Innovative Developments in Transportation Geotechnics

Publisher: Springer Nature Singapore

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Abstract

The use of ground-penetrating radar (GPR) technology in railway maintenance and inspection has gained significant attention in recent years due to its non-destructive and efficient nature. This paper explores the application and development of artificial intelligence (AI) in conjunction with GPR for the assessment of ballasted tracks. The integration of AI algorithms enhances the interpretation of GPR data, providing a more accurate and automated method for detecting and predicting potential trackbed fouling. This article explores the innovative applications of machine learning and deep learning in GPR data processing for railway track engineering prospection, evaluating their potential, limitations, and future prospects, aiming to contribute to the advancement of railway infrastructure monitoring, with a focus on improving reliability, safety, and maintenance efficiency.

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Fontul S, Paixão A, Solla M, Pajewski L (2018) Railway track condition assessment at network level by frequency domain analysis of GPR data. Remote Sens (Basel) 10(4). https://doi.org/10.3390/rs10040559

Guo Y, Liu G, Jing G, Qu J, Wang S, Qiang W (2022) Ballast fouling inspection and quantification with ground penetrating radar (GPR). Int J Rail Transp 1–18. https://doi.org/10.1080/23248378.2022.2064346

Wang H, Silvast M, Markine V, Wiljanen B (2017) Analysis of the dynamic wheel loads in railway transition zones considering the moisture condition of the ballast and subballast. Appl Sci (Switzerland) 7(12). https://doi.org/10.3390/app7121208

Oskooi B, Parnow S, Smirnov M, Varfinezhad R, Yari M (2018) Attenuation of random noise in GPR data by image processing. Arab J Geosci 11(21). https://doi.org/10.1007/s12517-018-4035-z

Rasol M et al (2022) GPR monitoring for road transport infrastructure: a systematic review and machine learning insights. Constr Build Mater 324. https://doi.org/10.1016/j.conbuildmat.2022.126686

Yue Y et al (2024) Automatic recognition of defects behind railway tunnel linings in GPR images using transfer learning. Measurement 224:113903. https://doi.org/10.1016/j.measurement.2023.113903CrossRef

Küçükdemirci M, Sarris A (2022) GPR data processing and interpretation based on artificial intelligence approaches: future perspectives for archaeological prospection. Remote Sens 14(14). https://doi.org/10.3390/rs14143377

Liu H et al (2023) Combined CNN and RNN neural networks for GPR detection of railway subgrade diseases. Sensors 23(12). https://doi.org/10.3390/s23125383

Wang Y et al (2022) RCE-GAN: a rebar clutter elimination network to improve tunnel lining void detection from GPR images. Remote Sens (Basel) 14(2). https://doi.org/10.3390/rs14020251

10.

Ni ZK, Shi C, Pan J, Zheng Z, Ye S, Fang G (2022) Declutter-GAN: GPR B-scan data clutter removal using conditional generative adversarial nets. IEEE Geosci Remote Sens Lett 19. https://doi.org/10.1109/LGRS.2022.3159788

11.

Brockner B, Dowdy JL, Anderson DT, Veal C, Scott GJ, Ball JE (2018) Generative adversarial networks for ground penetrating radar in hand held explosive hazard detection, vol 10628, pp 306–323. https://doi.org/10.1117/12.2307261

12.

Qin H, Zhang D, Tang Y, Wang Y (2021) Automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation. Autom Constr 130. https://doi.org/10.1016/j.autcon.2021.103830

13.

Dogan M, Turhan-Sayan G (2016) Preprocessing of A-scan GPR data based on energy features, vol 9823, pp 415–422. https://doi.org/10.1117/12.2223946

14.

Liu C, Du Y, Yue G, Li Y, Wu D, Li F (2024) Advances in automatic identification of road subsurface distress using ground penetrating radar: state of the art and future trends. Autom Constr 158:105185. https://doi.org/10.1016/j.autcon.2023.105185CrossRef

15.

Wang S, Liu G, Jing G, Feng Q, Liu H, Guo Y (2022) State-of-the-art review of ground penetrating radar (GPR) applications for railway ballast inspection. Sensors 22(7):2450. https://doi.org/10.3390/s22072450CrossRef

16.

Kim N, Kim K, An YK, Lee HJ, Lee JJ (2020) Deep learning-based underground object detection for urban road pavement. Int J Pavement Eng 21(13):1638–1650. https://doi.org/10.1080/10298436.2018.1559317CrossRef

17.

Sresakoolchai J, Kaewunruen S (2022) Railway defect detection based on track geometry using supervised and unsupervised machine learning. Struct Health Monit 21(4):1757–1767. https://doi.org/10.1177/14759217211044492CrossRef

18.

Wang X, Liu X, Euston TL (2023) Relationship between track geometry defect occurrence and substructure condition: a case study on one passenger railroad in the United States. Constr Build Mater 365. https://doi.org/10.1016/j.conbuildmat.2022.130066

19.

Basye C, Wilk S, Gao Y. Ground penetrating radar (GPR) technology evaluation and implementation

20.

Goodarzi S, Kashani HF, Oke J, Ho CL (2022) Data-driven methods to predict track degradation: a case study. Constr Build Mater 344. https://doi.org/10.1016/j.conbuildmat.2022.128166

Title: AI-Based Ballasted Track GPR Application and Development
Authors: Xuanyang Qin
Zhan Peng
Guoqing Jing
Publisher: Springer Nature Singapore
Book: Recent Advances and Innovative Developments in Transportation Geotechnics
Print ISBN: 978-981-9782-44-4

Electronic ISBN: 978-981-9782-45-1

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-981-97-8245-1_4

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