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![Principle of GPR detection technology for ballast layer (figure modified after [23]).](publication/359398890/figure/fig3/AS:1144325316591616@1649839824061/Principle-of-GPR-detection-technology-for-ballast-layer-figure-modified-after-23.png)
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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 becom...
Contexts in source publication
Context 1
... principle of GPR detection technology is shown in Figure 4. The transmitting antenna emits electromagnetic waves to the inside of ballast layer. ...
Context 2
... the waves encounter a boundary layer or area with different dielectric properties, the waves are reflected and scattered. Then, the receiver antenna records the return wave time and amplitude to form a single waveform, as shown in Figure 4. The single waveforms are obtained along the railway line, which can be gathered to form a two-dimensional radar image. ...
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Citations
... One of the devices used to determine the condition of the ballast is the Ground Penetrating Radar (GPR) [3,4]. It uses polarized high-frequency radio waves, usually in the range of several hundred MHz to several GHz. ...
The amount of freight transported by rail and the number of passengers are increasing year by year. Any disruption to the passenger or freight transport stream can generate both financial and human losses. Such a disruption can be caused by the rail infrastructure being in poor condition. For this reason, the state of the infrastructure should be monitored periodically. One of the important elements of railroad infrastructure is the ballast. Its condition has a significant impact on the safety of rail traffic. The unevenness of the ballast surface is one of the indicators of its condition. For this reason, a regulation was introduced by Polish railway lines specifying the maximum threshold of ballast unevenness. This article presents an algorithm that allows for the detection of irregularities in the ballast. These irregularities are determined relative to the surface of the sleepers. The images used by the algorithm were captured by a laser triangulation system placed on a rail inspection vehicle managed by the Polish railway lines. The proposed solution has the following elements of novelty: (a) it presents a simple criterion for evaluating the condition of the ballast based on the measurement of its unevenness in relation to the level of the sleeper; (b) it treats ballast irregularity detection as an instance segmentation process and it compares two segmentation algorithms, Mask R-CNN and YOLACT, in terms of their application to ballast irregularity detection; and (c) it uses segmentation-related metrics-mAP (Mean Average Precision), IoU (Intersection over Union) and Pixel Accuracy-to evaluate the quality of the detection of ballast irregularity.
... In this context, to assess conditions of the ballast layer by employment of stand-alone technologies, including fouling levels and moisture content, utilizing ground penetrating radar (GPR) emerges as a fitting non-destructive method [30]. In addition, employing imaging-based methods is invaluable for inspecting the condition of the land on which the railway is constructed. ...
Railway track health monitoring and maintenance are crucial stages in railway asset management, aiming to enhance the train operation quality and service life. For this aim, various inspection means (using diverse non-destructive testing techniques) have been applied, however, these means are mostly not able to monitor whole railway track network or track underlying layers (e.g., ballast and subgrade). The use of remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), can expedite the defect diagnosis process for railway tracks, elevating the scope of health monitoring to a network-wide level. The Ground Penetrating Radar (GPR) has emerged as a particularly reliable method, especially for detecting structural deficiencies in underlying layers. As a result, combining the two distinct non-destructive testing techniques – GPR and InSAR – presents a promising strategy for efficient railway asset management. Recognizing the significance of embracing newer and more advanced monitoring strategies, this paper reviews the fusion of GPR and InSAR methodologies, and explores the potential integration of machine learning models to develop a predictive health monitoring and condition-based maintenance approach for railway tracks.
... Earlier studies have primarily utilized ground penetrating radar (GPR) to detect railway subsurface defects [7][8][9]. However, GPR has a significant limitation: due to the high frequency and rapid attenuation of electromagnetic wave signals, along with susceptibility to the medium's electrical conductivity (significantly influenced by moisture) [10,11], its effective detection depth is generally limited to 3-5 meters. ...
To rapidly and effectively detect geological hazards in railway infrastructure subsurface, this paper proposes an approach combining two non-destructive testing technologies: B-ultrasound and ground penetrating radar. A new microtremor exploration method using B-ultrasound (MEMBu) is applied to collect natural microtremor signals from the railway infrastructure subsurface. Using the extended spatial autocorrelation calculation (SPAC) method and stationary stochastic signal processing, MEMBu can detect subsurface medium velocity structures and is better at detailed exploration of anomalous distributions of acoustic impedance in the subsurface. Ground penetrating radar (GPR) technology utilizes electromagnetic wave propagation characteristics in railway subsurface media. By detecting reflected electromagnetic waves and analysing their travel time (also known as round-trip time), amplitude, and frequency, it can infer the structure and depth of buried objects. To verify this novel monitoring/inspection approach, railway platforms, critical to railway operation efficiency and reliability, were tested. This paper analyses the principles, characteristics, advantages, and limitations of these two technologies and, through examples, demonstrates their application methods and effects in railway subsurface structures' inspections, ultimately providing new insights into railway health monitoring.
... Another domain where GPR has shown immense potential is in the inspection of railway ballast. A review by Wang et al. (2022) provided an overview of the state-of-the-art applications of GPR for railway ballast inspection. The study highlighted the challenges associated with ballast layer inspection and condition evaluation using GPR over the past two decades. ...
... The study highlighted the challenges associated with ballast layer inspection and condition evaluation using GPR over the past two decades. The research emphasized the potential of GPR in reflecting the ballast layer condition, such as fouling and moisture, by analyzing the variation in the received signal (Wang et al., 2022). In essence, the versatility of GPR, combined with advancements in technology and methodologies, has expanded its applications in various sectors. ...
This research aimed to delve into the potential of applied geophysics in environmental conservation, with a specific focus on the South West Nigerian context, and its implications for data analysis and policy implementation. Through a comprehensive review of peer-reviewed articles, case studies, and primary research, the study explored various geophysical techniques and their applications in environmental conservation efforts. The research highlighted the efficacy of these techniques in the South West Nigerian region, emphasizing their significance in shaping environmental policies. Despite the evident potential, challenges and limitations associated with these techniques were also identified. The study further provided suggestions for improved environmental conservation strategies and highlighted potential areas for continued research in the realm of applied geophysics. In conclusion, while applied geophysics offers promising tools for environmental conservation, a holistic approach, integrating both scientific and policy-driven strategies, is essential for sustainable conservation efforts in South West Nigeria. Recommendations include fostering collaborations between geoscientists, policymakers, and local communities, and investing in further research to address identified gaps. Keywords: Applied Geophysics, Environmental Conservation, South West Nigeria, Policy Implementation, Data Analysis.
... Subsequent researchers increased the frequency of GPR used for better resolution [8]. In addition, models for assessing the condition of railroad ballast and predicting the fouling of the ballast based on GPR are available in references [9][10][11]. GPR models for railroad ballast are costly and challenging due to the complex nature of the ballast and the variability of physical conditions. ...
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.
... However, these methods often lead to a rapid increase in the volume of radar equipment, resulting in the further deterioration of radar maneuverability. Meanwhile, it can lead to serious problems, such as a rapid increase in the overall manufacturing costs of the radar system and difficulties in later maintenance [15]. ...
Compared to the distributed ground-based radar (DGBR), the distributed airborne radar (DAR) has been widely applied due to its stronger anti-damage ability, more degrees of freedom, and better detection view of targets. However, unlike DGBR, the premise for the normal operation of DAR is to maintain stable wireless communication between unmanned aerial vehicles (UAVs). This requires each UAV to make reasonable use of its electromagnetic domain resources. That is, to maximize radar detection performance while ensuring communication performance constraints. However, current research in the field of radar resource allocation has not taken this into account, which greatly limits the practical application of optimization algorithms. Moreover, the current research tends to adopt centralized optimization algorithms. When the baseline of the UAV swarm is long, applying multi-relay methods directly results in heavy communications overhead and long-time delay. Based on the above background, this article aimed to develop a fully distributed algorithm for the joint optimization of radar detection performance and communication transmission performance. This study first took the measurement angle of arrival (AOA) as an example to provide a system model with communication constraints. This model considers the impact of factors such as the UAV location error, UAV communication coverage, and dynamic communication topology of the UAV on joint optimization. A formal representation of the joint optimization is presented. Then, we proposed a joint radar-communication optimization (JRCO) algorithm to fully utilize the electromagnetic domain resources of each UAV. Finally, numerical simulations verified the effectiveness of the proposed JRCO algorithm to traditional radar resource allocation methods.
... • Machine vision-based inspection is a current area of intense research. Technologies used include ground penetrating radar (GPR), LiDAR, and InSAR [23][24][25][26][27]. While these methods have been used to identify ballast fouling, analysing the resultant image data is very timeconsuming and requires experienced experts. ...
... The thickness of the clean ballast layer is a sensitive indicator reflecting the condition of the ballast layer [23]. This is because under cyclic loading, the ballast layer becomes fouled by various types of contamination. ...
... Earlier studies have demonstrated that the scattering characteristics of Ground Penetrating Radar (GPR) waves can be used to ascertain the porous structure of ballast layers. It has been proven that there is a strong correlation between the GPR-based fouling index (calculated based on radar detection data) and the actual fouling level of the ballast layer [4,23,47]. This suggests that GPR can effectively reflect the health condition of the ballast layer. ...
... Railway subgrade is a critical component of the railway system, essentially providing stable support for tracking (ballasted track, slab track), thus ensuring safe train operation [1]. However, with increased intensive high speed train loading, axle load, and traffic volume, the subgrade has undergone unacceptable rapid degradation, leading to frequent maintenance activities [2]. ...
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.
... 3D ground penetrating radar (GPR) maybe a promising to achieve this. More explanations about GPR application to railway ballast layer inspection can be found in [140][141][142]. ...
... Lastly, some important technologies that did not fit with the current paper structure include: automatic ballast sampler [212], PANDA/Geoendoscopy coupling [213], GPR [140], LiDAR [214] and trial holes [215]. ...
... 3D ground-penetrating radar maybe a promising 619 to achieve this. More explanations about ground-penetrating radar application to railway 620 ballast layer inspection can be found in [138][139][140]. 621 the mechanical properties of the ballast gradually transform from quasi-solid to quasi-fluid 651 (Table 6), which is consistent with the results obtained through field experiments. ...
Railway ballast performance is dictated by a complex mix of mechanical properties. These effect its performance at the particle level for example in terms of particle degradation, but also at the track system level in terms of settlement and stability. Therefore this paper seeks to develop new understandings of ballast behaviour and identify opportunities for future research directions. First, ballast particle size and size distribution curves are discussed, considering opportunities to improve breakage, settlement and drainage characteristics. Next, particle geometry is discussed, with a focus on form, angularity and surface texture. This is followed by a discussion on the degradation mechanisms of ballast particles and the effect of fouling on permeability. Next, techniques to assess and improve ballast bulk density are discussed such as ground penetration radar and dynamic track stabilisation. Testing methods for studying ballast are also reviewed, first considering both smaller-scale tests such as direct shear tests and the Los Angeles abrasion test. Then larger-scale laboratory testing is discussed, including large-diameter dynamic triaxial testing and the use of full-scale laboratory tracks. Finally, conclusions are drawn and suggestions for future research directions are given.
