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Ballast fouling occurs when the voids of granular particles are wholly or partially filled by fine particles. Railway ballast fouling is always an inevitable issue related to railway performance and safety in ballasted tracks. This paper presents an overview of the railway ballast fouling mechanism, current works, perspective, and innovation. The paper first reviews the fouling mechanism with different fouling sources and their influence, followed by the fouling indices. Then, fouling inspection methods are reviewed, and the ground penetrating radar is highlighted due to its high efficiency and accuracy. The proposed solutions, including rehabilitation, prevention, reinforcement, and stabilization in the ballasted track, are introduced. Additionally, several perspectives and works for the future are mentioned. Ballast cleaning is a thorough and effective treatment. Ground-penetrating radar inspection and proper evaluation can provide a preview and perspective on ballast cleaning determination. [ABSTRACT FROM AUTHOR]

Systematic assessment of ballast fouling and mechanized cleaning efficiency through ground penetrating radar (GPR) is vital to ensure track stability and safe train transportation. Nevertheless, conventional methods of ballast fouling inspection and evaluation impede construction progress and escalate the cost of maintenance. This paper proposes a novel method using random irregular polygons and collision detection algorithms to model the ballast layer and simulated using the finite-difference time-domain (FDTD) algorithm. Hilbert transform energy, S-transform, and energy integration curve are employed to identify ballast fouling and cleaning efficiency. The highly fouled ballast exhibits concentrated Hilbert transform energy, increased energy attenuation rate in S-transform with depth in the 1.0–3.0 GHz, along with a stronger energy integration curve. Clean or post-cleaning ballast shows opposite results. Experiments on a passenger trunk line in southern China validated the method's accuracy after mechanized ballast cleaning. This approach guides GPR-based detection and supports railway maintenance. Future studies will consider heterogeneous properties and the three-dimensional structure of the ballast layer. [ABSTRACT FROM AUTHOR]

Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation. [ABSTRACT FROM AUTHOR]

• Six diverse railway lines were inspected by the GPR, proving the GPR application feasibility for diverse railway line conditions. • Accuracy of the GPR-based fouling index is correlated with sieving-based fouling index. • By comparing the sensitivity of different indicators, the suitable GPR signal processing means is obtained. • Fouling composition is proved as a decisive factor for GPR-based fouling level evaluation, especially the iron ore. Ballast layer condition should be more regularly and accurately inspected to ensure safe train operation; however, traditional inspection methods cannot sufficiently fulfil this task. This paper presents a method of ground penetrating radar (GPR) application to reflect ballast layer fouling levels under diverse field conditions (annual gross passing load, cleaning and renewal year, fouling composition and transportation type). The results show that the GPR-based inspection method can assess the ballast layer fouling level with a 1–7% difference from the traditional sieving results. Fouling composition (especially metal materials) has a great effect on the GPR signals, thus affecting the inspection accuracy of ballast layer fouling level. Developing diverse GPR-based fouling indicators (by distinguishing different GPR signal features) can improve the GPR inspection applicability to the diverse field conditions. [ABSTRACT FROM AUTHOR]