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.@en

The railway ballast layer provides the function of bearing loading, resisting geometry degradation, and drainage. In those related research, the behaviour of ballast assembly can be obtained by laboratory (or in-situ) tests. Limited simulation methods can be used to analyse the behaviour of ballast particles at the mesoscopic level. The numerical simulations based on the Discrete Element Method (DEM) are employed, which treat every ballast particle as a calculation component. However, the efficiency of DEM simulation is very low due to the algorithm and a very large number of elements. This paper analysed the efficiency-related questions of the DEM modelling. The influence of particle shape and contact properties on the force behaviour is studied. Further, an optimised multi-layer ballast track model is introduced based on the most influential ballast areas. In such areas, particles are generated with an irregular shape to ensure the reliability of results, and particles except that area are generated with a rolling resisted ball shape to decrease the number of elements. A series of lateral resistance simulations are conducted to show and validate the accuracy and efficiency of this method in the dimension of the single sleeper section. Results show that this optimised multi-layer model building method largely improves efficiency, and it can provide accurate data.@en

Railway ballast beds bear cyclic loadings from vehicles and deteriorate due to ballast particle degradation (breakage and abrasion), ballast pockets (subgrade defects), fouling (or contamination) and plastic deformation of the beds. Ballast bed deterioration changes the ballast track geometry, which leads to uncomfortable rides, exacerbates wheel-rail interactions and, most importantly, causes safety issues (e.g., derailment). To align the track geometry, tamping is the most widely used means of filling ballast-sleeper gaps and homogenizing ballast beds. Although many studies have been performed on tamping, some necessary research gaps still need to be addressed. To stress the research gaps, tamping studies are critically reviewed in this paper, and the tamping mechanisms, challenges and proposed solutions are introduced and discussed. This review aims to 1) help researchers discover important research directions related to tamping, 2) propose means for tamping methodology improvement/development, and 3) provide advice for developing novel railway track maintenance.@en

The performance and deformation of ballast bed are significantly influenced by the particle morphology (size and shape), the rheology (translation and rotation), and the degradation (breakage and abrasion). Regarding the ballast particle morphology, the ballast particle size is generally measured by sieving and described with the Particle Size Distribution (PSD), while the particle shape is normally classified as three characteristics, the form, angularity, and surface texture. Quantifying particle morphology with current manual methods is difficult to obtain accurate results (often subjective). Concerning the ballast particle rheology, almost all the related studies are based on numerical simulations, e.g. the Discrete Element Method (DEM). A limited number of studies were performed to record the translation and rotation with the electronic devices embedded in ballast layer. However, the numerical simulations can only precisely reflect the ballast particle rheology in quasi-static tests (e.g. direct shear test), and the electronic devices can only record the ballast particle rheology in the limited areas, where they were placed. The ballast breakage could be evaluated by the change of the PSD, but the determination of PSD involves significant errors. Additionally, the manual methods could not fully quantify the ballast abrasion. As a result, more accurate evaluation methods need to be developed and utilised for the validation and confirmation of the degradation-related studies.Towards these limitations, the studies on two-dimensional (2D) and three-dimensional (3D) image analysis methods for granular materials are reviewed, discussing their existing and potential utilisation in railway ballast applications. This paper can be of interest to the researchers, who are dealing with the performance and deformation of ballast bed. Additionally, a special attention can be paid to utilising the image analysis for accurate particle morphology quantification, particle rheology investigation and ballast degradation evaluation.@en

Ballast track is the most widely used track for the railway transport, and ballast bed plays a significant role to provide resistances during train operation. Generally, the ballast bed consists of crushed stones. To achieve the mitigation of ballast degradation, the first priority is to describe the degradation development and to study its effect factors. The influence of ballast morphology (particle size and shape) on ballast degradation is examined here using the Los Angeles Abrasion (LAA) test in combination with 3-D image analysis. LAA tests are used to obtain the deteriorated ballast. Then, based on the 3-D images, the changes of ballast particles after the tests were analysed. To quantify the ballast degradation (abrasion and breakage), the Abrasion Depth based on the analysis of 3-D images were proposed, while ballast breakage was estimated using the broken particles ratio. The results have shown that ballast degradation is directly related to the ballast morphology. The proposed image-based procedure can effectively be applied to assess ballast degradation. The results can be used for ballast material standardization, modelling of ballast degradation process and maintenance cycle prediction.@en

The properties of railway ballast material are affected by the local geologies and climatic environments from which the parent rock is sourced. These factors can make it challenging to select the most appropriate material for railway applications. To address this issue, this paper first reviews the means of ballast selection in complex environments across the world. The selection criteria for ballast materials are compared and test methods for ballast quality quantification are summarised. Next, ballast parent rock types and the implications of mining approaches are discussed, before analysing ballast morphology with respect to ballast size and shape. Then ballast petrography is reviewed with a focus on the effect of mineral composition on performance. Finally, some promising future ballast technologies are discussed with a focus on environmental performance. These include recycled ballast, asphaltic materials, steel slag and ballast gluing. The review shows that regarding ballast selection means and criteria, the number and type of quantitative indicators varies greatly between countries. In particular there are divergences in test methods and quantitative indicators for ballast quality considering material types and local geologies. Suggested future research directions are proposed, such as the effect of tamping and dynamic track stabilisation on ballast properties.@en

The discrete element method (DEM) has been confirmed as an effective numerical method for modelling railway ballast, and successfully used to analyse a wide range of ballast-related applications (e.g. geomaterials). However, there still exists some aspects under development. Among them, the model calibration can be the most significant one (morphology, degradation and contact model). Because reliable and accurate results can be obtained only when the parameters are carefully selected. Therefore, diverse DEM applications and developments in railway ballast are critically reviewed. Furthermore, the model calibration methods are discussed. This is able to help future researchers improve the existing calibration methods, further, build more accurate, standardised and validated DEM models for ballast-related studies. Additionally, this paper can assist researchers to choose an appropriate model for specific applications.@en

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.@en

Parent rock strength and crumb rubber modification are two critical mechanical parameters that significantly decide the ballast layer degradation subjected to train dynamic loading. Using machine learning to predict ballast degradation considering these two parameters is helpful for deciding ballasted track maintenance cycle. In the current study, the ballast degradation process data (variables: parent rock types, loading types, ballast gradations and compositions of crumb rubber-ballast mixture) were used to train machine learning models. The drop-weight impact loading tests were performed to simulate different train dynamic loadings. Two well-established machine learning models, i.e., random forest (RF) and support vector regression (SVR) were trained and verified, to more effectively assess the importance of these variables. The results from the validated machine learning models confirm that the parent rock type is the most influential parameter, followed by the loading type (applied stress level), to control and predict the degradation of the ballast-CR mixture. The experimental assessment reveals that although the incorporation of CR suppresses degradation across all characterized rock types, the improvement in performance of the ballast-CR specimen against degradation is more noticeable for high-strength parent rock subjected to a considerable stress level. Meanwhile, this positive influence is also observed for ballast of weaker strength when the applied stress level is low.@en

Particle shape plays an essential role in deformation characteristics of railway ballast bed. The numerical reconstruction of ballast morphological features, including overall shape and angular distribution, remains a hot issue in research on ballast mechanical behavior simulation. A novel shape reconstruction method was adopted to generate ballast particles that met the desired probability density distribution of morphological indices. On this basis, the numerical model of ballast triaxial tests were established under different confining pressures. The results were compared with those obtained from indoor tests and simulations whose particles were generated from 3D scanning or non-statistical random generation. The results show that the particle shape has a growing effect on the mechanical response of ballast, with an increase in confining pressure. The relation between deviatoric stress and axial strain in the specimen which meets the probability density distribution is more consistent with the experimental results than that of the non-statistical randomly generated specimen. The lateral deformation of ballast is correlated with the adjustment of the packing structure. For non-statistical randomly generated specimen, both the lateral deformation and the particle adjustment are larger than those generated by 3D scanning. The ballast contact force evolution is less influenced by its morphological features. Nevertheless, the difference in the maximum contact force of specimens with various particle shapes is nearly 50%.@en

The occurrence of ballast contamination or fouling frequently results in a sudden decline in the capacity of railway ballasted tracks. Considering the various sources of ballast fouling, clay is the most severe one for causing a drastic reduction in the drainage capacity of the ballast layer. In the current study, we utilized a large-scale flume test to measure the water height along the cross-section of the clay-fouled ballast. Subsequently, an analytical–numerical (A-N) approach was developed to simulate the movement of water through porous media under steady-state conditions, while also considering the flow regime. This A-N approach was validated using the results of flume tests. Finally, the validated A-N approach was employed to generate a dataset and develop machine learning models for predicting water height. The characterized machine learning models included random forest regression (RFR), support vector machine (SVM), and extreme gradient boosting (XGBoost). Various variables, such as ballast gradation, fouling ratio, bed slope, rainfall rate, and water height on the side ditch, were incorporated into the machine learning models to reveal the contribution of each individual variable. Results show that for clean ballast, the incorporation of a nonlinear model between flow velocity and hydraulic gradient in the A-N approach is crucial to properly estimate the experimental measurements. However, a comparison of the water height measured via the flume test and the water level estimated based on the A-N approach confirms the suitability of the linear model, i.e., Darcy's law, for the water flow regime through clay-fouled ballast. According to the machine learning results, particularly those from the XGBoost model, which was characterized as the elite model, the rainfall rate and the fouling index emerged as the most influential variables affecting the water height in the clay-fouled ballast layer of the railway track.@en

This paper presents a multi-layer railway ballast track and substructure model, where a coupled discrete and continuous method is used for macro-meso dynamic behaviour analysis under moving wheel loads. In this coupled model, the discrete element method (DEM) is utilised to build the superstructure of the ballast track (i.e. rail, fastener, sleeper and ballast layer), which considers the complex ballast shape and particle size distribution from mesoscopic level. The finite difference method (FDM) is used to simulate the substructure (i.e. subgrade and foundation) with a consideration of computing cost. And then, the coupled model is achieved by satisfying displacement, velocity and contact force compatibility between the FLAC and the PFC. Finally, the dynamic analysis is carried out by applying the wheel-rail forces obtained from a vehicle-track dynamics model into the coupled model. The DEM parameters of ballast particles are calibrated based on the results of the ballast direct shear tests, and the dynamic behaviour of railway ballast track and subgrade system is validated with the field measurement. Through the numerical analysis, it is confirmed that the coupled DEM/FDM model can reliably reflect macro-meso dynamic behaviour and accurately reveal the contact characteristics between the ballast layer and subgrade.@en

Railway ballast is normally made of crushed rocks with grading (particle size distributions). Ballast is inevitably suffering from more rapid degradation. Because ballast keeps undergoing and dissipating most of the train loadings, furthermore, the train speed and freight weight are increasing, causing more intensive loadings to ballast. To prolong ballast service time and reduce ballast maintenance cost, more studies need to be performed on the ballast degradation reduction, ballast inspection, and ballast assessment. Therefore, earlier distinguished studies on these three aspects are introduced, summarized and discussed in this chapter, toward the final goal of providing research gaps, engineering guidance, and maintenance advice.@en

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.@en

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.@en

Differential railway track settlement can result in ballast voids, leading to sleepers that hang from the rail and are no longer supported by the ballast. These hanging sleepers are damage for track component. As a solution, this paper proposes and investigates a new concept sleeper with a wedge-shaped geometry, intended to stimulate the migration of ballast into any voids, thus reducing the occurrence of hanging sleepers. A series of scaled laboratory tests and 2D and 3D discrete element simulations are used to investigate different wedge-shaped geometries. The investigations include the wedge type (single long wedge versus multiple mini-wedges) and the wedge angle (30, 45, 60 degrees). First, the scaled laboratory tests are used to study the performance of different wedge geometries. Next, 3D DEM simulations are performed to analyse the contact forces in the ballast due to different wedge designs. Finally, 2D DEM simulations are performed to study the settlement behaviour. The main conclusions are that a single long wedge is preferable compared to multiple smaller wedges. when the wedge sleeper angle is larger than the ballast's angle of repose, particles have the freedom to migrate into the settlement induced voids. Also, an increased wedge sleeper angle stimulates greater particle migration and thus improves the support correction. However the longer wedge also leads to a decrease in effective ballast height under sleeper which may make retrofitting on existing lines challenging.@en

Ballast layer defects are the primary cause for rapid track geometry degradation. Detecting these defects in real-time during track inspections is urgently needed to ensure safe train operations. To achieve this, an indicator, the track degradation rate (TDR) was proposed. This rate is calculated using track geometry inspection data to locate and predict railway-line sections with ballast layer defects. The TDR is determined by the monthly standard deviation of the rail longitudinal level, which is one aspect of track geometry. The Ballast Layer Health Classification (BLHC) is designed by assessing the two successive TDRs before and after track geometry maintenance actions. The BLHC is used to categorize the conditions of the ballast layer, including normal periodic deterioration, abrupt deterioration, effective maintenance, rising deterioration, and severe deterioration. Both the TDR and BLHC were validated through field assessments of ballast layer conditions, where the two indicators were found to be effective in revealing defects. The results indicate that the TDR is sensitive to ballast layer defects, while the BLHC can quickly identify the location of these defects. Consequently, the BLHC can provide real-time guidance for ballast layer maintenance.@en

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.@en

Lateral and longitudinal resistance of ballasted track are two main indicators for the track stability quantification. Aiming at improving the lateral and longitudinal resistance, nailed sleeper is studied with single sleeper push tests (SSPTs) and discrete element modelling (DEM). The SSPTs were applied to study how much resistance the nailed sleeper can improve, considering different nail lengths (100, 200, 400 mm), and also used to calibrate and validate the DEM models. With the validated DEM models, different simulation conditions were performed and compared to confirm the optimal nail length (100, 200 mm) and nail number (2, 4). Results show that applying nailed sleepers improves the lateral resistance by 53.7% and the longitudinal resistance by 39.2%. 4 nails, compared to 2 nails, can increase lateral and longitudinal resistance by 20.2% and 10.6% (nail length: 100 mm) as well as 37.0% and 33.5% (nail length: 200 mm), respectively.@en

To enhance the stability of continuous welded rail (CWR) tracks, frictional sleepers have been developed. The frictional sleepers are new types of sleepers with grooves on the bottom, and different bottom grooves improve lateral resistances at different magnitudes. In this study, single sleeper push test (SSPT) and its model with discrete element method (DEM) were carried out to confirm how much arrowhead groove frictional (AGF) sleeper increases the lateral resistance of ballasted track. The SSPTs were performed to confirm the lateral resistance results, and also to validate and calibrate the DEM models. With the validated models, the groove factors influencing the lateral resistances were studied, including groove sizes (depth, width), arrowhead groove direction and groove numbers. The reason of lateral resistance improvement was studied at mesoscopic level, including the ballast-sleeper contact numbers and contact force chains. Results show that applying the AGF sleeper is able to improve lateral resistance by 7–24%, and it can provide enough lateral resistance after reducing ballast shoulder width from 500 mm to 300 mm. The AGF sleeper can improve the sleeper-ballast interaction by increasing sleeper-ballast contact number. The study is helpful for frictional sleeper design, further improving track stability.@en