This paper presents a big data-based analysis of the rail wear of the whole Belgian railway network measured in 2012 and 2019. Wear rates are reported, discussed, and quantitatively formulated as functions of critical factors in terms of curve radius, annual tonnage (rail age), high rail in curves, an average from both rails in straight tracks at rail top (vertical wear) and gauge corner (45° wear) and for steel grade R200 and R260. The influence of preventive grinding is also analysed. The wear rates are derived in an aggregated manner for the whole network. The wear rates do not show significant change with changes in rolling stock over the years, implying that the wear rates could also hold for other networks. It is found that R200 shows, on average, a 34% higher wear rate than R260. Also, the wear rate per tonnage is lower for high-loaded tracks. Thus, time is a relevant factor in explaining the wear evolution of low-loaded tracks; for instance, the effect of corrosion may have an important role. The paper provides statistically significant information that can be used for wear modelling, understanding and treating rolling contact fatigue based on the wear rate and developing tailored rail maintenance strategies.@en

In this paper, a fuzzy interval-based method is proposed for solving the problem of rail defect detection relying on an on-board measurement system and a multiple spiking neural network architecture. Instead of outputting binary values (defect or not defect), all data will belong to both classes with different spreads that are given by two fuzzy intervals. The multiple spiking neural networks are used to capture different sources of uncertainties. In this paper, we consider uncertainties in the parameters of spiking neural networks during the training phase. The proposed method comprises two steps. In the first step, multiple sets of the firing times for both classes are obtained from multiple spiking neural networks. In the second step, the obtained multiple sets of firing times are fuzzy numbers and they are used to construct fuzzy intervals. The proposed method is showcased with the problem of rail defect detection. The numerical analysis indicates that the fuzzy intervals are suitable to make use of the information provided by the multiple spike neural networks. Finally, with the proposed method, we improve the interpretability of the decision making regarding the detection of anomalies.@en

To investigate the train-induced ground vibration, an explicit time-domain, three dimensional (3D) finite element (FE) model is developed. The train, track, embankment, pile-board structure and nearby ground soils are all fully coupled in this model. The complex geometries involving the track components and pile-board structure are all modelled in detail, which makes the simulation of wave propagation more realistic from the train to the ground. The model is validated with in situ tests data collected in the Beijing-Shanghai high speed railway line. Good agreements have been achieved between the numerical results and experimental results both in time domain and frequency domain. The proposed model is thus capable of reproducing the dynamic ground response induced by a typical high speed train. Soil responses induced by different number of vehicles are compared. With more vehicles, the spectral peaks of soil responses are more prominent at the integral multiples of the vehicle passing frequency. Too few vehicles will not bring about such phenomenon, thus sufficient number of vehicles should be included in a train to properly model train-induced ground vibration. With the proposed model, the influence of the pile-board foundation on the ground vibration is investigated. It is found that the pile-board foundation can significantly attenuate the low frequency ground vibration. The attenuation of the ground vibration as a function of distance from the track is simulated and the influential factors to the local vibration amplification are investigated. It is found that soil Young's modulus and soil impedance contrast are the two main factors influential to the local vibration amplification. The softer the natural soil, the larger the amplification. The larger soil impedance contrast makes the amplification more obvious. The soil stratification and geometric discontinuity at ground surface are not the main cause of the local vibration amplification in this work.@en

In this paper, we present a solution method based on finite element (FE) modeling to predict multimodal dispersive waves in a free rail. As well as the modal behaviors and wavenumber-frequency dispersion relations, the phase and group velocities of six types of propagative waves are also derived and discussed in detail in the frequency range of 0–5 kHz. To experimentally distinguish different types of wave modes, the operating deflection shape (ODS) measurement approach is employed in the laboratory. ODS is measured from the spatial distribution of imaginary parts of the FRFs. We also propose a synchronized multiple-acceleration wavelet (SMAW) approach to experimentally study the propagation and dispersion characteristics of waves in a free rail. The group velocities in the vertical, longitudinal and lateral directions are estimated from the wavelet power spectra (WPSs). The good agreement between the simulation and measurement in terms of mode shapes and ODSs, wavenumber-frequency dispersion curves, and group velocities indicates that the ODS and SMAW approaches are capable of distinguishing different wave modes and measuring wave propagation and dispersion characteristics. In situ experimental results further demonstrate the effectiveness of the ODS measurement for coupled modal identification and the SMAW approach for wave dispersion analysis of the rail in a field track.@en

This paper proposes a new hypothesis for the formation process of short pitch rail corrugation. An FE wheel-track dynamic model is utilized to verify the hypothesis by reproducing corrugation initiation and consistent growth. It is found longitudinal compression modes are responsible for corrugation initiation with necessary initial excitation that allows flexibility for longitudinal vibration. Consistency between longitudinal compression and vertical bending eigenfrequencies of the wheel-track system is required for consistent corrugation growth, which also determines maximum corrugation amplitude. Corrugation initiates by frequency selection instead of wavelength fixing. The proposed mechanism can explain field observations including the wavelength and periodicity of corrugation in the Netherlands, why corrugation forms on continuously-supported tracks where pinned-pinned resonance does not exist, and the small variation between the corrugation wavelength and train speed.@en

While various train-borne techniques have been developed for measuring railway track stiffness, differentiating stiffness at different track layers remains a challenge. This study proposes a digital twin framework for the vehicle–track interaction system, which enables track stiffness evaluations based on axle box accelerations (ABA). The digital twin consists of a physics-based model, a model library and data-driven models. Compared to existing techniques, the proposed method simultaneously evaluates the stiffness of the railpad, sleeper and ballast layers at a sleeper spacing resolution, while being robust to varying track conditions, such as track irregularities and vehicle speeds. This is accomplished by employing a localized frequency-domain ABA feature capable of distinguishing between the characteristics of different track layers. Furthermore, track stiffness is evaluated in near real-time. This is achieved using a model library derived from physics-based simulations of a range of track conditions. Two data-driven models that can quickly select or interpolate model instances contained in the library are developed. During operation, the data-driven models use the measured ABA features as input and then infer the stiffness for the different track layers. The proposed method is applied to evaluate the track stiffness of a downscale test rig in a case study. The track stiffness evaluated by the proposed method is compared with that obtained through hammer tests and with the observations of the track component conditions. These comparisons show that the proposed method can capture the stiffness variations due to periodically fastened clamps and substructure misalignments at different speeds. In addition, the proposed method is demonstrated to be superior to the commonly used hammer test method for evaluating track stiffness under loaded conditions.@en

In this paper, we present preliminary results on the development of smart technology solutions for lower density railway lines. The goal is to reach a cost effective inspection and asset management to minimize maintenance interventions time/cost without dedicated inspection vehicles. The proposed methods include axle box acceleration measurements and ultra-low cost smartphones. The collected data will be further used to increase knowledge of the condition of the railway track and to estimate the comfort of passengers. In order to make use of the data, the data is interpreted and converted from monitoring information into management information. Feasibility and preliminary studies were conducted in the railway lines of Romania. The results presented in this paper were obtained in the framework of the H2020 project NeTIRail-INFRA, Work Package 4: Monitoring and Smart Technology.@en

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using Hilbert-Huang Transform, the locations where the major risks are detected and ssessed with a degradation model. Finally, evolutionary multiobjective optimization is employed to solve the maintenance decision problem, and to facilitate the visualization of the trade-offs between number of interventions and performance. Real-life measurements from the track from Braşov to Zărneşti in Romania are included to show the methodology.@en

Weld-induced squats are a major damage type in high-speed railways as well as in conventional railways. They incur high maintenance costs and endanger operational safety. This paper first presents and analyzes five-year continual field monitoring observations and measurements of squats at rail welds. A hypothesis of the formation and development process of the squats is proposed, which includes three steps. Steps 1 and 2 are pre-cracking, and Step 3 is post-cracking. To verify the pre-cracking process, a three-dimensional (3D) finite element (FE) model is then built up to simulate the vehicle-track interaction with detailed consideration of the local wheel-rail frictional rolling contact. Not only dynamic contact forces but also plastic deformation and wear are calculated. Starting from a smooth rail surface with varying yield stress derived from field-measured hardness, the numerical analysis confirms the hypothesis that the varying hardness at heat-affected zones (HAZs) leads to initial V-shaped irregularities due to differential plastic deformation. Afterward, the surface irregularities excite the dynamic longitudinal contact force, which in turn produces a W-shaped surface pattern through further differential plastic deformation. The growth of the W-shaped pattern leads to the formation of squats. This work provides insight into the squat formation process at rail welds and suggests that welding quality control in terms of hardness variation in the HAZs could reduce or even avoid squats. Early detection of squats with dynamics-based methods is possible.@en

This study presents a measuring framework for railway transition zones using a case study on the Swedish line between Boden and Murjek. The final goal is to better understand the vertical dynamics of transition zones using hammer tests, falling weight measurements, and axle box acceleration (ABA) measurements. Frequency response functions (FRFs) from hammer tests indicate two track resonances, for which the FRF magnitudes on the plain track are at least 30% lower than those at the abutment. The falling weight measurements indicate that the track on the bridge has a much higher deflection than the track on the embankment. Two features from ABA signals, the dominant spatial frequency and the scale average wavelet power, show variation along the transition zone. These variations indicate differences in track conditions per location. Finally, the ABA features in the range of 1.05–2.86 m−1 are found to be related to the track resonance in the range of 30–60 Hz. The findings in this paper provide additional support for physically interpreting train-borne measurements for monitoring transition zones.@en

Railway transition zones connecting conventional embankments and rigid struc-tures, such as bridges and tunnels, usually degrade much faster than other railway sections. Efficient health condition monitoring of transition zones is important for preventative track maintenance. In this paper, a methodology for monitoring rail-way transition zones using acceleration measurements on multiple axle boxes (multi-ABA) of a passing train is presented. To showcase its capability, the measurements in the Netherlands, Sweden, and Norway are analyzed and dis-cussed. It is found that different bridges and transition zones exhibit unique char-acteristics including dominant wavelengths and energy distribution. Based on these unique characteristics, the geometry and support conditions at different lo-cations of a transition zone can be evaluated. Higher train speed makes the char-acteristics more pronounced. The results demonstrate that the multi-ABA meas-urement has the potential to evaluate and thus monitor the health conditions of various transition zones.@en

Railway transition zones connecting conventional embankments and rigid struc-tures, such as bridges and tunnels, usually degrade much faster than other railway sections. Efficient health condition monitoring of transition zones is important for preventative track maintenance. In this paper, a methodology for monitoring rail-way transition zones using acceleration measurements on multiple axle boxes (multi-ABA) of a passing train is presented. To showcase its capability, the measurements in the Netherlands, Sweden, and Norway are analyzed and dis-cussed. It is found that different bridges and transition zones exhibit unique char-acteristics including dominant wavelengths and energy distribution. Based on these unique characteristics, the geometry and support conditions at different lo-cations of a transition zone can be evaluated. Higher train speed makes the char-acteristics more pronounced. The results demonstrate that the multi-ABA meas-urement has the potential to evaluate and thus monitor the health conditions of various transition zones.@en

Railway transition zones connecting conventional embankments and rigid struc-tures, such as bridges and tunnels, usually degrade much faster than other railway sections. Efficient health condition monitoring of transition zones is important for preventative track maintenance. In this paper, a methodology for monitoring rail-way transition zones using acceleration measurements on multiple axle boxes (multi-ABA) of a passing train is presented. To showcase its capability, the measurements in the Netherlands, Sweden, and Norway are analyzed and dis-cussed. It is found that different bridges and transition zones exhibit unique char-acteristics including dominant wavelengths and energy distribution. Based on these unique characteristics, the geometry and support conditions at different lo-cations of a transition zone can be evaluated. Higher train speed makes the char-acteristics more pronounced. The results demonstrate that the multi-ABA meas-urement has the potential to evaluate and thus monitor the health conditions of various transition zones.@en

This work investigates the power dissipation in a wheel/rail system through friction work modeling. In order to identify the effect of the friction coefficient on the power dissipation in the wheel/rail contact, several simulations were performed using a three-dimensional multibody model of a railway vehicle implemented in the software package VI-Rail Adams. The power dissipation and wear rates of the inner and outer wheels of the first bogie of the vehicle running over a curve of a metro line were calculated for different friction coefficients varying from 0.2 to 0.7. The total frictional work was obtained from the resultant force and slip in a reference point. The wear was also analyzed according to the Tγ method including the spin, in combination with Kalker׳s simplified theory Fastsim, assuming that the wear is proportional to the frictional work. Two sets of rail and wheel profiles were studied in order to determine the effect of the profile׳s quality on the power dissipation and wear rates. To this end simulations and power calculations were performed with a friction coefficient of 0.4.@en

A field sample of rail steel was metallurgically examined to characterize its rolling contact fatigue (RCF) damage. In addition to the well-known white etching layer (WEL), a possible different type of surface modification layer was identified in parallel. The layer has some similar features as the WEL but exhibits a significantly different etching response to 3 vol% Nital etchant. After etching, the new layer exhibits a brown color under the same light reflection. This layer was named as “brown etching layer” (BEL) to distinguish it from the WEL. Similar to the WEL, cracks are observed to be closely related to the BEL. The cracks are found to penetrate deeper than those initiated by the WEL reported in existing publications. Further, they are found to propagate downwards without branching, which may eventually cause rail fracture. Although its formation mechanism is not yet clear, WEL has been considered by some authors in the literature as a possible RCF initiation source. It is therefore of critical importance to understand the characteristics of the BEL and its formation mechanism. This may also lead to better understanding of the formation mechanism of the WEL. To this end, microstructural features of the BEL were studied using micro-hardness tests, optical microscopy and scanning electron microscopy. The BEL was found to be distinctly softer than the WEL and lamella-type features are found within the BEL. The microstructural features of the BEL were compared with the WEL reported in the literature. Finally, the formation mechanism of the fatigue damage was discussed based on the comparison, observations and material characterization.@en

The railway industry has the potential to strongly contribute to achieving various sustainable development goals by expanding its role in the transportation system of different countries. To realize that, complex technological and societal challenges are to be addressed, along with the development of suitable state-of-the-art methodologies fully tailored to the particular needs of the wide variety of railway infrastructure types and conditions. Artificial intelligence (AI) methods have been increasingly and successfully applied to solve practical problems in the railway infrastructure domain for over two decades. This paper proposes a review of the development of AI methods in railway infrastructure. First, we present a survey limited to selected journal papers published between 2010-2022. Bibliographical statistics are obtained, showing the increasing number of contributions in this field. Then, we select key AI methodologies and discuss their applications in the railway infrastructure. Next, AI methods for key railway components are analyzed. Finally, current challenges and future opportunities are discussed.@en

The railway industry has the potential to strongly contribute to achieving various sustainable development goals by expanding its role in the transportation system of different countries. To realize that, complex technological and societal challenges are to be addressed, along with the development of suitable state-of-the-art methodologies fully tailored to the particular needs of the wide variety of railway infrastructure types and conditions. Artificial intelligence (AI) methods have been increasingly and successfully applied to solve practical problems in the railway infrastructure domain for over two decades. This paper proposes a review of the development of AI methods in railway infrastructure. First, we present a survey limited to selected journal papers published between 2010-2022. Bibliographical statistics are obtained, showing the increasing number of contributions in this field. Then, we select key AI methodologies and discuss their applications in the railway infrastructure. Next, AI methods for key railway components are analyzed. Finally, current challenges and future opportunities are discussed.@en

In this paper we discuss rail condition monitoring based on axle box acceleration (ABA) measurements. We present three case studies. The first one in The Netherlands, the detection of local defects with different severity levels (squat A, squat B and squat C) is analysed. The second case from the Faurei testing ring in Romania, the detection of rail defects over the whole testing ring is presented and examples of responses at a local defect (wheel-burn) is discussed with measurements at 80km/h (conventional speed measurement) and 200km/h (high speed measurement). In the third case, ABA measurements were obtained during operation in a train with passengers in the railway line near Brasov, Bartolomeu-Zărneşti. Examples of the defects and validations are discussed.@en