Assessment of ballast layer under multiple field conditions in China

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.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Railway Engineerin

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

Ground penetrating radar (GPR) has been applied for ballast layer inspection for two decades, mainly for the analysis of ballast layer fouling levels. However, some issues that affect the inspection quality remain unsolved, such as issues involving the GPR equipment quality (antenna) and the correlation between the GPR indicator and fouling index. With the aim of solving these two issues, in this paper, we investigated the difference between the results of two different antennas, the GPR data processing technique, indicators for the fouling level (by GPR signal processing) and the correlation between the indicators and fouling index (obtained by sieving). The results show that the antenna quality determines the inspection quality. The indicators can reflect the ballast layer fouling level, and they correlate the best with the fouling index (obtained by the percentage of particles passing through a 5 mm sieve size). This study is helpful for the future modification of railway ballast maintenance standards.Railway Engineerin

State-of-the-Art Review of Ground Penetrating Radar (GPR) Applications for Railway Ballast Inspection

In the past 20 years, many studies have been performed on ballast layer inspection and condition evaluation with ground penetrating radar (GPR). GPR is a non-destructive means that can reflect the ballast layer condition (fouling, moisture) by analysing the received signal variation. Even though GPR detection/inspection for ballast layers has become mature, some challenges still need to be stressed and solved, e.g., GPR indicator (for reflecting fouling level) development, quantitative evaluation for ballast fouling levels under diverse field conditions, rapid GPR inspection, and combining analysis of GPR results with other data (e.g., track stiffness, rail acceleration, etc.). Therefore, this paper summarised earlier studies on GPR application for ballast layer condition evaluation. How the GPR was used in the earlier studies was classified and discussed. In addition, how to correlate GPR results with ballast fouling level was also examined. Based on the summary, future developments can be seen, which is helpful for supplementing standards of ballast layer evaluation and maintenance.Railway Engineerin

Railway ballast layer inspection with different GPR antennas and frequencies

Ground penetrating radar (GPR) is a popular technology for inspecting railway ballast layer, mainly on the ballast fouling level. However, different GPR antennas with different frequencies are suitable for different inspection emphasis and diverse railway lines (weather and sub-structure). In addition, the full-scale track model (with subgrade) for experimental tests was not seen in earlier studies. For further application of GPR in China, the GPR inspections (with 400 MHz, 900 MHz and 2 GHz antennas) were performed on a 30 m long full-scale track and three railway lines (different weather and sub-structure). Results show that ballast layer inspection should be performed mainly with the 2 GHz antenna and supplemented by the 400 MHz and 900 MHz antennas. The weather has great influence on the results of GPR inspection. This study is helpful for supplementing the guidance of ballast layer inspection with GPR.Railway Engineerin

Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

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.Railway Engineerin