This study presents a surrogate-based multi-objective nose shape optimization for a 400 km/h-class high-speed train, considering normal open-air, crosswind open-air, and tunnel-related scenarios. The KTX-Cheongryong nose shape is parameterized using Bézier curves and a section box approach to satisfy dimensional constraints specified in relevant technical regulations. The objective functions are the tail car drag coefficient under normal open-air conditions, the leeward rail rolling moment coefficient under a 40 m/s crosswind, and a micro-pressure wave-related proxy based on the circumferential integration of wall-normal velocity on an imaginary tunnel wall. A trackside pressure variation limit of 700 Pa at 250 km/h is imposed as a constraint. A dataset comprising 544 designs was generated using three-dimensional compressible steady Reynolds-averaged Navier–Stokes simulations and used to construct Gaussian process regression surrogate models. These models are coupled with the non-dominated sorting genetic algorithm II to obtain Pareto-optimal solutions, all of which dominate the KTX-Cheongryong across the three objectives. Extreme designs on the Pareto front, selected as boundary solutions for each objective, are compared with the corresponding single-objective optima to clarify objective trade-offs. The representative compromise design achieves an 8.9
As the requirements for railway operators to offer a safe and economically efficient transportation service have become extremely difficult to met due to the size and complexity of the rail network, vehicle-based track monitoring and fault detection have gained relevance as a way to abate maintenance-related costs via condition-based and predictive approaches. In this work, a cost-effective, permit-free vehicle-based track monitoring system implemented on a regular in-service vehicle is presented. The designed system makes use of inertial sensors mounted on the vehicle’s bogie and car body, as well as positioning technology based on global navigation satellite system (GNSS) to collect monitoring data for detection and spatial localization of track defects. The capabilities of the developed system are exemplified by means of a case study related to a track section with a temporary speed restriction (TSR)—a scenario where traditional acceleration-based detection often fails due to reduced vehicle speed. Here, it is demonstrated that the proposed approach, combining statistical and time-frequency analysis (e.g., wavelets), can effectively lead to the detection and localization of anomalies on the track using onboard inertial measurements, even under reduced vehicle speed.
Assessing aerodynamic modifications in high-speed trains requires quantifying drag reduction relative to previous designs and evaluating the improvement potential of an integrated configuration. This study investigated three-car configurations of the KTX-Cheongryong and HSEMU-370 platforms using 4.3
The detection of foreign objects in key components of high-speed trains is critical for railway safety, but existing methods struggle under low-light conditions, complex backgrounds, and small objects. To address these issues, we propose an efficient detection framework integrating an adaptive brightness enhancement network (ABEN) and a lightweight train foreign object detection network (LTFD-Net). ABEN adaptively enhances images according to their illumination, improving clarity across multiple objects and backgrounds while ensuring real-time processing. LTFD-Net combines a lightweight backbone with a multi-dimensional feature enhancement module, capturing multi-scale and contextual features to accurately detect small and complex defects with minimal computational overhead. To support realistic evaluation, we introduce the high-speed train foreign object detection (HTFD) dataset with 3,904 annotated images across five key components. Experiments show that the integrated framework achieves 85.4
Crosswind is one of the key factors threatening the safety and stability of high-speed train operations. Due to the highly stochastic nature of natural wind fluctuations, traditional analysis methods struggle to accurately capture the full probabilistic information of system responses. This paper introduces the probability density evolution method (PDEM) to conduct an in-depth study on the stochastic vibration response of a high-speed vehicle–bridge coupled system under crosswind. First, a dynamic analysis model for the vehicle–bridge coupling system considering the randomness of aerodynamic loads is established. The stochastic wind load acting on the vehicle–bridge system is simulated using a non-stationary assumption, where the wind speed process is described as a spatially varying stationary Gaussian random field. Based on this, the probability density evolution method is applied by introducing random variables that characterize the essential randomness of the wind field, and a generalized probability density evolution equation is constructed to relate the system state quantities to these random variables. Using the multiple-distribution point selection method, a representative set of points is selected, ultimately obtaining the probability density function (PDF) and its evolution process of the stochastic dynamic response of the system. Taking a long-span cable-stayed bridge as an example, numerical simulations accurately reveal the complete evolution of the PDF of the system response in the time domain under crosswind. This method provides a more comprehensive and accurate theoretical tool and analytical approach for assessing the operational safety risks of high-speed trains crossing long-span bridges in crosswind environments.
This paper compares the assessment of freight train running safety according to two European rules, namely the UIC IRS 40421 and the EN 14363, under emergency braking operations. The paper relies on a combination of longitudinal train dynamics (LTD) simulations for the evaluation of the in-train forces and multibody (MB) simulations for the calculation of the running safety indexes derived from the wheel-rail contact forces. The results confirm that the IRS 40421 is more conservative when assessing the safety of families of freight trains with a statistical perspective. Nonetheless, in individual operating scenarios, the IRS 40421 can predict safe conditions when MB simulations calculate running safety indexes above the limits prescribed by the EN 14363. To improve the running safety assessment from the outputs of LTD simulations, the paper suggests the introduction of kernel regressions, acting as digital twins of MB simulations, for the prediction of the running safety indexes. Digital twins improve the consistency with the classification of derailment conditions based on the outputs of the MB simulations. The findings highlight the complementary value of the two rules and the potential of digital twins to support the reliable assessment of freight train running safety.
Temperatures reached on railway wheels tread during braking with composite blocks are responsible for extensive damage unknown when cast iron brake blocks were used. The accurate measurement of wheel tread temperature during braking becomes then fundamental to tune models to estimate thermomechanical damage due to the combined thermal and vertical/longitudinal actions at the wheel/rail contact especially in fully loaded conditions. This measurement is not trivial during service braking, as contact (sliding) thermocouples are not reliable in the long term and conventional infrared sensors in the wavelength range 8–14 μm are not suitable for bright steel surfaces. Moreover, thermal cameras with the correct parameters are expensive and too delicate to be installed even temporarily on a freight wagon bogie frame. A low-cost, non-contact measuring device based on a rugged single-spot pyrometer sensitive to the correct infrared emission wavelength (1.5–1.8 μm) was developed. The pyrometer is mounted on a motorized slider continuously spanning the tread surface for a practically unlimited time. The functionality of a prototype was checked during braking sessions on a homologated brake rig, tuning the actual wheel tread emissivity with the help of specific finite element thermal simulations. The device was then mounted on a Y25 bogie and in-service measurements were performed. The paper describes the development and calibration of the device, concluding with the results of the in-service tests.
The wear of wheels and rails has a significant impact on the dynamic performance and safety of railway systems. Reliable prediction models are essential for effective maintenance planning. This study examines the effect of surface contamination on the friction and wear behaviour of commonly used wheel and rail steels. Twin-disc rolling–sliding experiments were performed to assess the effects of creep ratio, contact pressure, and tangential speed on friction coefficient and wear. Samples were cut from the rail head and locomotive wheel tyre, in the form of rollers and shoes. Various creep ratios were obtained from roller-on-roller tests on rollers with different diameters. To obtain pure sliding, the tests were conducted on a rotating roller with a stationary shoe. Under dry roller–roller conditions, the friction coefficient stabilizes within the range μ ≈ 0.168–0.236, with typical values of approximately 0.19–0.20 at 10
This paper presents the development and validation of an automatic onboard monitoring system installed on a locomotive for railway infrastructure condition assessment. The system integrates a strategic layout of accelerometers mounted on axles, bogies, and the carbody, combined with triggering mechanisms for accurate structural monitoring of track sections. Real-time solutions for data acquisition, storage, and transmission enabled long-term deployment under operational conditions. A case study was conducted on a 450-m track section on Ferrovia Tereza Cristina, in Brazil, comprising both intact rails and areas with spalling, squats, and severe wear with plastic deformation. Two defect detection methodologies were evaluated: one based on time-frequency domain features using continuous time wavelet transform, and another relying on time-domain autoencoder modeling. Both methods effectively distinguished damaged from intact sections and demonstrated sensitivity to defect severity, with higher damage indices observed for severely damaged rail sections. The results confirm the robustness of the monitoring system and the potential of data-driven approaches for accurate and continuous railway condition assessment.
The mechanical behavior of the train–track system plays a pivotal role in defining relevant operational parameters, such as train speed, comfort, and safety. In sections where the railway subgrade is well compacted, train circulation is smoother and more stable, with lower levels of displacement during operation. However, railway transition zones between embankments and viaducts often show significant stiffness changes, which can lead to differential settlements, larger deflections, and higher maintenance needs over time. This study develops and experimentally calibrates a finite-element computational model to simulate the mechanical response of such transition zones. The model is calibrated using track deflection measurements obtained at only three instrumented locations under trains operating at multiple speeds on an active Brazilian railway. Numerical–experimental comparisons identify the governing parameters affecting track response within the transition region. In addition, a spatial stiffness-variation function is formulated and its parameters are calibrated using the same measurement set. The results allow a more accurate representation of train–track interaction and provide guidance for the design and maintenance of durable, safer railway transition zones.
With the development of high-speed railways, the impact of sudden wind loads in the vicinity of bridge towers on train safety and stability has become a critical concern. To mitigate crosswind effects, the optimal design of wind barriers must consider not only wind fields and aerodynamic characteristics but also train dynamic responses. This study combines wind tunnel experiments and computational fluid dynamics simulations to analyze the effects of wind barrier parameters on wind fields, train aerodynamics, and dynamic performance. Using a wind–train–bridge coupling vibration model, the study investigates how barrier parameters influence driving performance indicators such as derailment coefficient, wheel load reduction rate, and carbody acceleration. The results indicate that wind barriers can effectively reduce the shielding effect of bridge towers, enhance flow field stability, and reduce wind loads and lift forces on the train. A lower porosity and optimal barrier height can enhance wind resistance, but beyond a height of 3 m, there is little change in the wind resistance effect. Moreover, an appropriate wind barrier length helps to reduce dynamic response fluctuations, lower acceleration peaks, and improve safety and comfort during train operation.
In railroad wheel climb L / . -0pt V derailment criteria, it is assumed that the direction of the relative velocity of the flange-contact point in its totality is downward, leading to an upward friction force along the wheel flange profile. That is, the longitudinal component of the relative velocity of the flange-contact point is assumed negligible. It is further assumed that the climb progresses along a flange profile, and consequently, the flange-contact point trajectory is defined by the flange profile curve or a straight line. This paper examines these two basic assumptions by developing a three-dimensional wheel climb model that relaxes the assumptions used in deriving the planar L / . -0pt V ratio. The results obtained show that, in case of flange contact at a large angle of attack, there is a nonzero longitudinal velocity component of the wheel flange-contact point, and the trajectory of this point is not, in general, defined by the flange profile or a straight line. Consequently, the phenomenon of wheel climb at a large angle of attack is three-dimensional, demonstrating the need for further investigations of the derailment criteria based on planar analysis.
With the rapid expansion of metro networks in China, the cumulative length of shield tunnels constructed in soft soil has exceeded 6000 km. While extensive engineering experience has been accumulated, these tunnels are still increasingly affected by service-related issues, such as long-term settlement, deformation, structural damage, and water leakage. These defects impose challenges to both operational safety and maintenance costs. This study provides a systematic overview of the major defect types and their spatial distribution patterns, highlighting their implications for the resilience and safety of shield tunnels. The coupled development and interaction of these defects are analyzed, and the limitations of existing research methodologies are critically examined. Based on these findings, this paper introduced a novel load mode to consider service tunnel’s environmental load variations, thereby proposing insights for enhancing the resilience of shield tunnel design from the tunnel–soil interaction perspective. Meantime, an elastic–plastic resistance model is also developed to address the degradation of lateral resistance at the tunnel waist caused by the fluidity of soft soils. A mathematical formulation of system stiffness is further developed by treating the tunnel and soil as an integrated system. Building upon this formulation, a resilience‑based design method is proposed to ensure the resilient performance of shield tunnels throughout the entire life cycle. The method is validated through its application to the Foshan and Shaoxing metro systems, with results demonstrating that optimizing system stiffness can significantly improve the resilience of shield tunnel structures in soft soils.
This paper investigates the fatigue reliability of the interfacial bonding in the concrete–concrete composites of CRTS III slab ballastless track structure under material uncertainty, using combined parametric experiments and numerical simulations. First, based on the fatigue constitutive model of interfacial bonding, parametric experiments are conducted to identify basic variables. Then, by integrating existing data, uncertainty quantification is performed for both the bonding and concrete to obtain the probability distributions of random variables. Moreover, the probability density evolution method is adopted to assess the interfacial fatigue reliability, based on finite element simulations of CRTS III slab ballastless track under fatigue temperature loading. The experimental results confirm the presence of significant material uncertainties, which underscores the necessity of shifting from a deterministic to a stochastic perspective for addressing the interfacial fatigue. The simulation results reveal that the interfacial fatigue reliability drops rapidly with service time. When the formulated level-II damage limit is employed as the maintenance criterion, the reliability decreases to 0.80, 0.48, and 0.17 after 10, 30, and 60 years, respectively. These findings indicate relatively weak durability of the interfacial bonding in concrete–concrete composites of CRTS III slab ballastless track and highlight the need of significant efforts from maintenance departments.
Railways form a vital part of global transportation networks, supporting economic development and sustainability through the efficient movement of freight and passengers. Maintaining the safety and reliability of these systems requires consistent inspection and monitoring of both infrastructure and rolling stock. Traditional inspection practices, while established, remain labour-intensive, time-consuming, and prone to human error, which can undermine operational reliability. Computer vision has become a key technology for automating these processes by enabling accurate visual assessment of tracks, components, and operational environments. Developments in deep learning have significantly strengthened these capabilities by improving defect detection accuracy, enhancing robustness under varied environmental conditions, and supporting real-time operation. More recently, AI-driven inspection frameworks that incorporate multi-sensor fusion, edge computing, and transformer-based architectures have pushed railway monitoring towards predictive, scalable, and increasingly autonomous maintenance solutions. This review provides a comprehensive and structured synthesis of research spanning track inspection, rolling stock monitoring, and passenger and operational safety. It evaluates recent methodological advances, summarises the strengths and limitations of current approaches, and identifies ongoing challenges related to data availability, computational constraints, and deployment in dynamic outdoor settings. The evidence indicates that track inspection technologies are closest to large-scale practical adoption, while rolling stock and safety monitoring systems are developing rapidly but still require further refinement for widespread deployment. By offering an integrated assessment of the current technological landscape and outlining opportunities for future research, this review supports researchers, industry practitioners, and policymakers in progressing towards safer, more efficient, and more sustainable railway systems enabled by modern computer vision and AI techniques.
Heavy haul railway operations present significant maintenance challenges, particularly accelerated wear and Rolling Contact Fatigue (RCF) of wheels and rails. Measures like tighter maintenance limits, optimized wheel–rail profiles, and advancing maintenance technologies have helped mitigate RCF on tangent tracks, large-radius curves, and high (outer) rails of small radius curves. However, these efforts have been less effective in mitigating RCF on low (inner) rails of small-radius curves. Given a fixed infrastructure design, rolling stock fleet, and optimized wheel–rail profiles, variations in operational conditions and the progressive degradation of wheels and track significantly influence wheel–rail interaction. The literature highlights that wheel hollowness and track gauge widening are the primary contributors. Therefore, this study focuses on an in-depth examination of how variations in these two factors influence wear and RCF development. A multibody dynamic model of an iron ore wagon is developed using the GENSYS software. Measured track irregularities, and rail and wheel profiles representing various degraded conditions, are incorporated into the simulations. The results reveal that wear number and RCF index trends differ significantly between the two rails (high and low rails) of a curved track, with degradation in wheels and rails. Consequently, maintenance strategies primarily designed to address high rail wear, since it is typically more severe, do not fully mitigate issues on the low rail.
Rolling stock manufacturers are increasingly developing innovative structural solutions aimed at enhancing the quality and reliability of railway vehicle components, thereby enhancing the existing standard platforms. Structural optimization processes represent an effective strategy to reduce manufacturing costs by promoting geometries that are simpler to design and fabricate. While structural optimization is now a well-established practice in the railway sector, the integration of fatigue considerations into this process remains limited. Although several studies in the literature attempt to address fatigue through various approaches, none have proven entirely satisfactory. This research aims to bridge this gap by introducing a novel methodology capable of automatically computing fatigue-related parameters, thereby enabling a parallel fatigue performance evaluation throughout the entire optimization process, iteration by iteration. The methodology is implemented via a dedicated software tool, which can be adapted with minimal modifications to interface with most commercial finite element platforms. The proposed approach has been applied to the structural optimization of a metro bogie frame. The methodology was then employed in multiple activities: iterative fatigue monitoring during the optimization process; extension of a previous study by incorporating fatigue behavior; reconstruction of the final post-optimization geometry based on fatigue-driven design considerations, thus ensuring manufacturability and fatigue resistance. Although not all potential uses have been fully explored, the proposed methodology has demonstrated its effectiveness as a valuable tool for integrating fatigue into structural optimization, ultimately enabling the designer to reconstruct fatigue-aware final geometries.
This paper offers a comprehensive review of the literature on the role of wheel–rail contact mechanics in train derailments. Using experimental and simulation methods, this review examines various research efforts that analyze how contact forces influence wheel climb and derailment dynamics. The related studies are summarized, and insights are provided on how they have contributed to understanding derailments and enhancing overall rolling stock safety. The review shows significant progress across different specialized areas within the broader topic of derailments. This includes advanced, state-of-the-art testing rigs, high-fidelity models that accurately replicate field conditions, materials that help prevent derailments, and wheel and rail profiles that reduce derailment risks. However, the accuracy of testing and modeling often requires more complex setups, sophisticated data analysis techniques, and greater resources. Despite these advances over the past few decades, further scientific research is necessary to understand better the root causes of events like wheel climb derailments under controlled and repeatable conditions.
Railway curve squeal is a significant source of environmental noise, arising from friction-induced instabilities in the wheel–rail contact. These instabilities are influenced by prevailing friction conditions which are affected by environmental factors such as humidity and temperature. This study presents a statistical analysis of long-term curve squeal measurements from a curve operated by commuter trains in Sweden. The analysis focuses on the relationship between environmental variables and squeal occurrence, distinguishing between squeal generated on the low and high rail. The results reveal distinct differences in squeal tendencies. Low rail squeal is most likely during relative rail humidity 55
