Based on the Satellite Positioning satellite navigation system, this paper uses the Satellite Positioning high-precision GNSS monitor to monitor the surface displacement and deformation of a tunnel slope. The monitoring data are transmitted back to the monitoring platform in real time through the transmission module, and the monitoring data are processed and analyzed. The whole process of automatic monitoring is realized, and the unstable sliding and flood disasters of the mountain body are predicted, so that the treatment and reinforcement measures can be taken as soon as possible. The purpose of ensuring the safety of tunnel operation is achieved. The field monitoring results show that: (1) The Satellite Positioning surface displacement monitoring system has high positioning accuracy. The plane accuracy is better than ±2.5 mm, and the vertical accuracy is better than ±5.0 mm. The shortest location update time is less than 1 s, and the success rate of one time location is better than 95%. (2) The receiving equipment in the Satellite Positioning surface displacement monitoring system can form the Internet of Things. The wireless network and the monitoring center server are used to realize information transmission. (3) After setting a reasonable instantaneous early warning value and an accumulated early warning value through expert evaluation, If the monitoring data value triggers the instantaneous early warning value or the cumulative early warning value, the system will automatically alarm, so as to ensure the safety of driving. (4) Through the analysis of monitoring data, the horizontal displacement value is within 10 mm, and the vertical displacement value is within 50 mm. The data is relatively stable and there is no landslide risk.
Track subgrade condition is one of the major aspects that influences overall track support conditions and railway dynamic response. However, current vehicle-based railway monitoring solutions do not provide reliable data on this, mostly due to an overall focus on the more accessible superstructure elements (e.g., rails, sleepers or the ballast layer). A new methodology to assess railway track support condition is currently under development and assessment, which is based on modal analysis of the characteristic frequencies of the multi-element system composed by an instrumented rail vehicle and the railway infrastructure under assessment. During this process, an unsupervised data-driven learning tool was developed to apply the proposed track monitoring methodology, enabling the automatic extraction of relevant features from the collected data. This tool was implemented to improve the reliability of the proposed methodology when working with information either from experimental data or numerical simulations. This paper provides an overall description of this track monitoring methodology and the current unsupervised data-driven tool. It also presents two cases-studies that were used to assess the developed solution, in terms of the theoretical concepts behind the methodology and the data processing tool itself. The first case-study is based on simulated data obtained from a numerical model created in Simpack ® , and the second is based on experimental data. The obtained results demonstrate the overall potential of this methodology to provide a valuable method to enable adequate subgrade condition monitoring.
Non-contact measurement using laser sensors for wheel profile inspection is commonly employed in railway infrastructure. However, two primary challenges arise: first, the deviation of the laser beam from the sensor’s axis, which leads to affine distortion in the measured wheel profile; and second, the interference from ambient light and contaminants, such as soil, on the wheel surface, which introduces ineffective points into the measurement data. To address these challenges, a robust scaling iterative alignment (RoSIA) method is proposed for wheel profile inspection. Initially, the Majorize-Minimization method is employed to construct a surrogate function for the registration objective. Second, the Welsch error metric is incorporated to enhance robustness against nonlinearities and outlier points. Third, a gradient-based optimization method is introduced to correct the affine distortion of the measured profile. The proposed method is comprehensively evaluated against conventional Iterative Closest Point (ICP) and Scaled Iterative Closest Point (SICP) algorithms in terms of accuracy, stability, and noise resistance. Field experiments confirm that the proposed RoSIA method performs well in both wheel shape calibration and noise resistance, demonstrating its potential for practical application in measuring wheel profile wear.
Squats on rails are a major contributor to maintenance costs in modern railway systems, yet their root causes and varying prevalence between networks are not fully understood. This study presents a harmonised statistical framework that enables international comparison of railway systems and quantifies squat risk and its influencing factors using a Cox proportional hazards model. The methodology is applied to data from the DACH region (Germany, Austria, Switzerland), considering parameters from railway operation, infrastructure management and track monitoring. Results show that the combination of low freight shares, low daily loads and hard rail grades in straight track most significantly increases squat risk. Predicted squat probabilities differ systematically between networks, with OEBB showing the lowest and SBB the highest values. The proposed model enables squat risk estimation based on five widely available input parameters and provides insights to support the optimisation of maintenance efforts and long-term system design.
Solid stick top-of-rail (TOR) products are widely used to reduce wheel squeal and wear at the wheel–rail interface. In current on-board systems, these products are typically applied continuously to the wheel tread using passive, spring-loaded mechanisms that have proven effective and robust in long-term railway operation. However, increasing operator demand for extended maintenance intervals places strong constraints on stick durability, particularly in high-speed operation where frictional heating and wear of the resin matrix become critical. Furthermore, standard continuous application results in unnecessary material consumption and particle generation in areas where friction modification is not required. Intermittent application, with adjustable contact force and application time, represents a promising strategy to address these limitations. Still, the influence of these parameters on solid stick behaviour has not been previously studied. The present study quantifies, using two commercial solid-stick products under laboratory conditions, how contact force and application time affect friction performance, layer retentivity, transfer behaviour, material consumption, and electrical impedance. The results show that increasing either parameter leads to a four-to fivefold increase in effective retentivity without compromising traction or braking safety. The wheel–rail impedance remained well below the threshold associated with loss of vehicle detection under all tested conditions, including higher contact forces. In contrast to liquid TOR products, the transfer of the friction layer between the contact surfaces was found to be negligible regardless of slip or contact force, with important implications for friction management system design and applicator deployment. A simplified analytical case study based on a tramline demonstrated that intermittent application can reduce material consumption by a factor of 7.7 compared to continuous application.
Heat generation in tread braking systems increases the temperature of wheel and shoes, possibly triggering damage phenomena. Therefore, reliable brake temperature predictions are essential. Numerical models can thrust the implementation of brake monitoring algorithms and the development of shoe materials with improved performances. However, experimental data is essential for tuning and validation purposes. The paper shows the tuning and validation of a finite element brake shoe thermal model against temperature values recorded with a thermal camera during tests performed with a new scaled twin-disc. The simplified model matches the experimental records with good accuracy.
The disassembly and collection of railway fasteners are critical components of track maintenance, directly influencing the efficiency, safety, and sustainability of railway operations. Currently, manual operations are hindered by high labour intensity and safety risks, while semi-automatic equipment remains limited by its reliance on manual positioning. Specialized machinery often suffers from low collection rates and poor compatibility across diverse fastener types. Although various robotic disassembly technologies have been proposed, existing solutions face significant challenges, including coaxiality deviations between the end-effector and the nut assembly, and insufficient compensation for angular and positional misalignments during high-speed execution. To address these issues, this paper proposes a novel fully automated railway fastener disassembly and collection system. The system integrates a specialized pneumatic end-effector with a universal joint limiting mechanism designed for angular deviation compensation and high-speed swing suppression. Furthermore, a modular mechanical architecture comprising six core functional modules is developed to realize a continuous material handling sequence from disassembly to centralized storage. A test platform simulating realistic railway operating conditions was constructed to conduct comparative experiments under varying lateral offsets. The results demonstrate that the proposed configuration achieves a 100% operational success rate under lateral deviations of 0–2.0 mm and maintains an 80% success rate at a maximum deviation of 3.0 mm, significantly outperforming conventional configurations. These findings provide a robust technical reference for the intelligent transformation of railway maintenance by enhancing operational reliability and reducing manual intervention.
To address ballast bed sliding and structural instability in rack railways operating on steep gradients, this study developed a coupled Multi-Flexible-Body Dynamics–Discrete Element Method (MFBD-DEM) model to investigate the mechanism by which an enhanced steel sleeper improves the longitudinal stability of ballasted track. Cyclic train loading was further applied to the DEM ballast bed model to simulate the evolution of track condition over the service period. The results show that increasing gradient weakens the longitudinal load-carrying capacity of the ballast bed, whereas the enhanced sleeper improves both longitudinal and lateral track stability by strengthening the sleeper–ballast interface constraint. Further analysis indicates that ballast particles undergo continuous migration under the combined action of downslope gravity and dynamic loading, leading to thinning of the upper ballast bed and an increased risk of local track deformation. Increasing the initial compactness level of the ballast bed effectively reduces particle mobility, thereby mitigating structural settlement and enhancing overall stability. Based on the analysis, the ballast bed density prior to commissioning of lines with a 480‰ gradient should not be lower than 1.67 g/cm 3 , and it is recommended to maintain a density of 1.70 g/cm 3 or above. This recommended value is also expected to satisfy the operational requirements of rack railway lines with lower gradients.
With the continuous growth in global energy demand, many industrial sectors are seeking effective strategies to reduce energy consumption. The transport sector is no exception. In railway applications, aerodynamic resistance represents one of the main contributors to energy consumption. Although most railway lines worldwide are dedicated to conventional trains, aerodynamic research has largely focused on high-speed vehicles. This study aims to address this gap by providing a methodology for estimating the aerodynamic resistance of conventional railway vehicles. The approach combines wind tunnel experiments on a scaled model with CFD simulations, enabling the extension of the analysis to full-scale open-air conditions and different train configurations. The results show good agreement between experimental and numerical data, supporting the reliability of the proposed approach. At full scale, the aerodynamic drag is found to be influenced by train configuration, travel direction, and pantograph arrangement. A comparison with running resistance measurements, based on the Davis formulation, indicates that the numerical predictions remain within the experimental uncertainty bounds, with a maximum deviation of approximately 8%. Overall, the study demonstrates that validated CFD simulations can provide reliable estimates of aerodynamic resistance and support design improvements. The results highlight a significant potential for enhancing the aerodynamic performance of conventional railway vehicles, with consequent reductions in energy consumption.
Surface microstructures have shown potential for drag reduction, but their effectiveness on maglev trains remains unclear. This study numerically investigates a 1:20-scale TR08 maglev train with hemispherical dimples arranged on either the streamlined head (HeadConcave) or tail (TailConcave), using large-eddy simulation at 400 km/h to evaluate aerodynamic force, surface pressure behavior, and surrounding wake flow. Results show that TailConcave achieves the best overall performance, reducing the total drag coefficient to 0.1597, about 2.0% lower than the Prototype, mainly through a 2.7% reduction in pressure drag, while HeadConcave increases drag by about 1.5%. Surface-pressure analysis indicates that head dimples intensify local suction and recirculation, promoting earlier separation, whereas tail dimples lower the tail pressure coefficient, smooth pressure recovery, and weaken pressure fluctuations near the tail shoulder, symmetry plane, and guideway-side surface. Wake-flow analysis further shows that TailConcave delays tail separation, extends the dominant vortices downstream, and reduces Reynolds shear stress and turbulent kinetic energy in the near wake. Overall, rear-surface dimples provide a more effective drag-reduction strategy than front-surface dimples.
Failures in wheelsets and bearings are a leading cause of train derailments and reduced system availability. Condition monitoring allows detection of developing faults and estimation of the remaining useful life of components. Recent advances in computer-based fault detection have improved the diagnosis of railway assets such as wheelsets, axles, bearings, and tracks. Industry 4.0 technologies, including big data analytics, cybersecurity, and the Internet of Things, have enhanced detection accuracy, efficiency, and real-time decision-making. However, since Industry 4.0 is so focused on technology, it often ignores things that are important to people. The new idea of Industry 5.0 combines human knowledge with intelligent systems to make maintenance plans that are long-lasting and flexible. This study evaluates the efficacy of railway component defect detection and diagnostic methodologies within the framework of Industry 4.0, and it delves into the possibilities and threats associated with the shift to Industry 5.0 for future railway maintenance systems.
Train collisions are important because they are more likely to result in high consequence events compared to the two most common incident types on U.S. railroads, derailments and grade crossing collisions. On average, train collisions result in more casualties, cars derailed, and monetary damages per incident owing to involvement of multiple trains and the often-higher levels of kinetic energy. In this study, we analyzed train collision data from the U.S. Department of Transportation, Federal Railroad Administration (FRA) from 1997 to 2024. In addition to the collision types defined by FRA, we identified two new types of collision, derailment-caused and handbrake failure. We quantified the frequency, rate, and severity of train collisions and developed statistics on train types involved, type of trackage where they occurred, and the different causes. We developed an algorithm to identify striking and struck trains in collisions and quantified the differences in their characteristics. The extensive empirical FRA data on train collisions enabled us to define, identify, and quantify the different roles of trains in collisions with a resolution not previously achieved. Train collision rates declined during the time period considered in this study; however, several types of collision remained relatively unchanged. Collisions that positive train control (PTC) is intended to prevent began to decline 15 years before PTC was fully implemented on U.S. railroads. PTC is expected to eliminate most of these types of collision on lines so equipped; however, several important collision types are unaffected by PTC, particularly collisions caused by derailments. The causes of collisions on mainline/siding and yard/industry tracks differ for these two operating environments so different mitigation measures will be needed to reduce collision risk. The results provide new insights and understanding of train collision risk and will inform more effective approaches to improve train safety.
Railway sleepers, while made by significantly stronger concrete than in conventional construction, often end up crushed forsteel recovery, leaving large amounts of concrete fragments underutilised and discarded in landfills. This study explores thefeasibility of incorporating sleeper recycled aggregate concrete (SRAC) into railway ballast, offering a sustainable solutionfor reusing 2-5 million tonnes of discarded sleepers annually. The research involves extensive laboratory testing, includingLos Angeles and Micro-Deval abrasion tests, water absorption assessments, and mechanical evaluations using point load andballast box tests. The ballast box tests subjected samples to 100,000 loading cycles to analyze settlement, ballast breakage,stiffness, and damping ratio. Results indicate that a mixture of 25% SRAC and 75% stone ballast provides an optimal balanceof performance and durability for railway lines. For this mixture, the ballast box tests showed a settlement of 9.8 mm, aballast breakage index of 0.0661, and a stiffness of 88,096 N/mm, all within acceptable ranges for railway ballast applications.The study also highlights the significant economic and environmental benefits of adopting SRAC in ballast applications,supporting sustainable railway infrastructure development.
Track irregularities critically impact railway operational safety, necessitating quantitative analysis. Existing geometric methods perform spatial domain analysis via angular domain resampling but rely heavily on precise synchronous speed data. Engineering challenges—such as sensor clock asynchrony, sampling discrepancies, and motion tracking delays—prevent reliable spatiotemporal synchronization between speed and vibration responses, causing phase shifts during feature separation. To address this without external sensors, we propose a method that synchronizes speed by extracting time-frequency ridge lines from axlebox vibrations. First, the Octave Linear Chirplet Transform calculates the time-frequency representation to obtain an initial ridge line. Then, based on the initial ridge line information, more accurate ridge line extraction parameters are obtained, and the time-frequency ridge line is further corrected by the Adaptive Fast Path Optimization algorithm. Finally, abnormal ridges are corrected using the ridge-speed proportional relationship and line conditions, enabling synchronous speed signal extraction directly from vibration data. The method’s efficacy is validated through simulations and experiments.
Curved railway tracks are susceptible to lateral instability due to the combined effects of centrifugal forces, reduced ballast confinement, and thermally induced stresses in continuous welded rails. These factors increase the risk of track misalignment, rail-seat deterioration, and potential buckling, particularly under high-speed and high-temperature operating conditions. To address these challenges, this study proposes a novel steel-plated grooved section (SPGS) anchorage system, designed to improve lateral stability by enhancing the mechanical interlock between prestressed concrete sleepers and the surrounding ballast. The SPGS anchors are easily removable, compatible with standard maintenance practices, and suitable for retrofitting. Three SPGS anchors, varying in anchor lengths (10 cm, 13 cm, and 16 cm), were evaluated through a combination of experimental single-tie push tests and discrete element method (DEM) simulations. Additionally, the interaction between the SPGS-anchored sleeper and ballast particles was analyzed using DEM to examine the contact force chains and energy dissipation within the ballast particles. The results showed that SPGS-anchored sleepers increased lateral resistance by 195% to 256% compared to unanchored sleepers, with the 13 cm anchor length demonstrating the most effective length within the tested range, providing the most practical and efficient performance among the tested anchor lengths. DEM analysis revealed a deeper and more uniform distribution of contact force chains, along with increased energy dissipation, which contributed to reduced ballast displacement and enhanced structural stability. The proposed SPGS system significantly increases the lateral resistance capacity, allowing higher safe operating speeds on curved tracks, as validated through safety margin calculations and lateral force demand assessments. These findings suggest that the SPGS anchorage is a practical and scalable solution to enhance the lateral resistance of ballasted railway tracks, particularly in curved sections.
With the continuous elevation in operating speed and traffic density of rail transit systems, dropper failures induced by high-frequency vibration and elevated stress levels have become increasingly prominent. However, field service data and mechanism-oriented studies on boltless integral droppers remain limited, leading to an inadequate understanding of their failure modes and underlying mechanisms. In this study, fatigue tests and finite element simulations were combined to investigate the failure characteristics and mechanism of boltless integral droppers. Fatigue tests show that the heart-shaped ring exhibited progressive wear-through at the contact interface with the wire clamp. Surface grooves, microscopic cracks, and cross-sectional delamination indicate that the damage process is governed by fatigue wear under repeated impact-sliding motion. A simplified finite element (FE) wear model based on Archard law was established to describe the material-removal component of this process. Under the investigated conditions, the model reproduced the same damage location and the same increasing trend of wear rate with tensile force as observed in the tests. Parametric studies show that higher tensile force and larger relative slip accelerate the wear of the heart-shaped ring. This study provides a feasible approach for wear simulation of boltless integral droppers and offers a useful basis for structural optimization and condition-oriented maintenance of railway catenary systems.
This paper presents a descriptive, data-driven model based on track settlement rates to support turnout design decisions. Given the wide range of turnout configurations, infrastructure managers must select designs that exhibit favourable long-term behaviour. Descriptive models can support this process by combining empirical in-field data with objective mathematical metrics. Key challenges arise from non-normally distributed settlement rates and strong interdependencies between turnout features, which are addressed using non-parametric methods, scaling, and targeted subsetting. The approach is demonstrated using settlement rates derived from 25 years of longitudinal level measurements recorded by regular track recording vehicles, covering 446 turnouts on the Austrian railway network. Driving direction is shown to have no influence on overall settlement behaviour. Sleeper type, by contrast, exhibits a clear and statistically robust effect: concrete sleepers equipped with under-sleeper pads show about halved settlement rates compared to conventional concrete sleepers and reduced by 40% compared to wooden sleepers. The effects of turnout radius and turnout type are physically plausible and consistent across subsets, but not always statistically significant due to the high variability inherent in field data. Despite remaining limitations, the proposed model provides a transparent and scalable basis for comparing turnout designs within a national network and across infrastructure managers. From an engineering perspective, it enables the identification of design configurations associated with reduced settlement behaviour and maintenance demand. When combined with life-cycle cost models, it represents a step toward data-driven decision support for more informed and economically efficient turnout design decisions.
Wheel-rail noise is a critical environmental constraint on rail transit development, with curve squeal noise generated on small-radius curves being particularly prominent. This noise features high frequency and sound pressure levels, making it highly penetrating and far-traveling, significantly degrading the acoustic environment. It exhibits a dominant frequency near the wheel-rail contact, arising from self-excited vibrations caused by lateral and longitudinal creepage. However, current approaches are limited by oversimplified lab models, an inability to isolate noise sources in field tests, frequency-domain models that cannot predict time-domain amplitude, and some time-domain models that often ignore acoustic radiation. In this context, this study develops a wheelset-track interaction model that accurately simulates both contact dynamics and acoustic radiation. The model validation shows strong agreement with field tests in both time and frequency domains, and the increase in adhesion coefficient also aligns with established literature. The results indicate that squeal noise can be mitigated by up to 3.8 dB through a reduction of the static friction coefficient via modifiers, while changes to the dynamic coefficient have little effect. The noise is found to be asymmetric between the inner and outer sides, a finding that underscores the necessity of a wheelset-track model. Moreover, as lateral creepage increases, inner wheel-rail noise dominates up to a ratio of 3.5%, beyond which it declines, and the outer wheel-rail noise becomes the dominant source. These insights establish theoretical foundations for metro curve squeal noise mitigation strategies.
Rainfall-induced landslides represent one of the most critical natural hazards affecting railway infrastructure in Italy, where complex geological settings and increasing climate-driven extremes challenge the reliability of transport services. This paper presents the SANF-RFI system, a national-scale early warning and decision-support platform developed through the collaboration between Rete Ferroviaria Italiana (RFI) and the Italian National Research Council - Institute for Geo-Hydrological Protection (CNR-IRPI). SANF-RFI adapts the national landslide early warning framework (SANF) to railway-specific requirements. It integrates near-real-time and forecast precipitation data with territorial susceptibility, railway exposure models, and quality-controlled rainfall observations. The system provides probabilistic estimates of rainfall-induced landslide triggering at the level of railway segments and sections, explicitly accounting for uncertainty related to rainfall measurement, spatial representativeness, and short-term forecast variability. After describing the system architecture, data flows, and probabilistic algorithms, the paper illustrates an operational application along the Marradi-Faenza railway line, where SANF-RFI enabled safe and flexible traffic management under severe hydro-meteorological conditions. The experience demonstrates how scientifically grounded early warning tools can enhance infrastructure resilience while maintaining essential railway services.
