Search Results (186)

In this study, with the rapid development of the field of rail vehicles, the laser welding process with high energy and small thermal deformation is selected, which reduces the working hours of post-welding grinding, repainting, and other processes, and ensures the industrial design requirements of the beautiful body after welding. The welding process for the non-penetration laser lap welding of SUS301L stainless-steel plates was optimized to address the problem of welding marks on the outer surface of railway vehicle car bodies. The impact of laser power, welding speed, and defocusing amount on weld penetration and tensile shear load was investigated using the response surface methodology. The results showed that the optimal response model for tensile shear load was the linear model, while the optimal response model for weld penetration was the 2FI model. The defocusing amount had the greatest influence on tensile shear load and weld penetration. When the laser power was 1.44 kW, the welding speed was 15 mm/s, and the defocusing amount was −4 mm, the tensile shear load reached its maximum by prediction. The actual tensile shear load of welded joints using these parameters was 4293 N with an error of merely 0.31% relative to the predicted value. The shear strength of laser-welded joints was measured at 429.3 N/mm, meeting the criteria established by the relevant standards. The tensile fracture shows characteristics of brittle fracture. The surface of the welded joints was bright white and well-formed, while the back side of the lower plate exhibited no signs of melting or welding marks. The microstructure of the weld zone (WZ) exhibited irregular columnar austenite and plate-like ferrite, while the heat-affected zone (HAZ) comprised columnar austenite and elongated bars or networks of δ-ferrite. The small-angle grain in welded joints can reduce grain boundary defects and mitigate stress concentration. After welding, angular deformation occurred, resulting in a residual stress distribution that shows tensile stress near the weld and compressive stress at a distance from the weld. Full article

Traditional diode-based rectifiers (TDRs) in railway traction substations (TSSs) are inefficient at handling bidirectional power flow and cannot recover regenerative braking energy (RBE). Replacing these conventional systems with reversible traction substations (RTSSs) requires detailed modeling, extensive simulations, and validation using real data. This paper presents a DT-oriented real-time modeling and Hardware-in-the-Loop (HIL) platform for the analysis and performance assessment of RTSSs in DC railway systems. The integration of interleaved PWM rectifiers enables bidirectional power flow, allowing efficient RBE recovery and its return to the main grid. Modeling railway networks with moving trains is complex due to nonlinear dynamics arising from continuously varying positions, speeds, and accelerations. The proposed approach introduces an innovative multi-train simulation method combined with low-level transient and power-quality analysis. The validated DT model, supported by HIL emulation using OPAL-RT, accurately reproduces real-world system behavior, enabling optimal component sizing and evaluation of key performance indicators such as voltage ripple, total harmonic distortion, passive-component stress, and current imbalance. The results demonstrate improved energy efficiency, enhanced system design, and reduced operational costs. Meanwhile, experimental validation on a small-scale RTSS prototype, based on data from the Italian 3 kV DC railway system, confirms the accuracy and applicability of the proposed DT-oriented framework. Full article

Accurate detection of concealed items in X-ray baggage images is critical for public safety in high-security environments such as airports and railway stations. However, small objects with low material contrast, such as plastic lighters, remain challenging to identify due to background clutter, overlapping contents, and weak edge features. In this paper, we propose a novel architecture called the Contrast-Enhanced Feature Pyramid Network (CE-FPN), designed to be integrated into the YOLO detection framework. CE-FPN introduces a contrast-guided multi-branch fusion module that enhances small-object representations by emphasizing texture boundaries and improving semantic consistency across feature levels. When incorporated into YOLO, the proposed CE-FPN significantly boosts detection accuracy on the HiXray dataset, achieving up to a +10.1% improvement in mAP@50 for the nonmetallic lighter class and an overall +1.6% gain, while maintaining low computational overhead. In addition, the model attains a mAP@50 of 84.0% under low-resolution settings and 87.1% under high-resolution settings, further demonstrating its robustness across different input qualities. These results demonstrate that CE-FPN effectively enhances YOLO’s capability in detecting small and concealed objects, making it a promising solution for real-world security inspection applications. Full article

Accurate prediction and management of train dwell times are essential for achieving efficient and sustainable public transport operations. This study evaluates established statistical dwell-time models within the context of Victoria’s regional railway network, contrasting their predictions with empirical data from video-based observations. Historically, these models—rooted in linear and non-linear regression analyses—have been designed for urban settings in peak periods. However, their applicability to regional railways, characterised by lower service frequencies with unique infrastructure and operational constraints, has been underexplored. The models were assessed for their ability to predict both passenger flow time and total dwell time under regional operating conditions. Results show that while passenger flow time can be predicted with moderate accuracy (best model R² ≈ 0.65), total dwell time models perform considerably worse (best model R² ≈ 0.25), largely due to unmodelled operational delays. The analysis identifies door operation cycles and conductor procedures as the primary operational variables influencing variability in total dwell time. Additionally, variations in passenger behaviour between peak and off-peak periods affect model performance. The findings underscore the need to incorporate local operational and behavioural factors into dwell-time models to enhance their predictive reliability for regional rail contexts. This study provides an empirical foundation for refining dwell time modelling approaches, supporting policymakers and operators in improving scheduling efficiency and overall service sustainability in regional rail networks. Full article

This study examines the influence of perceived inclusion and accessibility dimensions on user satisfaction within the Valparaíso Metro system in Chile. The research focuses on a quantitative survey conducted with 192 regular passengers along the Limache–Puerto corridor of the EFE Valparaíso railway network. A structured questionnaire comprising 58 Likert-scale items assessed perceived accessibility, inclusion, intermodality, safety, environmental effectiveness, and overall satisfaction. Data were analyzed using Confirmatory Factor Analysis (CFA) with the WLSMV estimator based on polychoric correlations, followed by multiple linear regression with robust standard errors. Results show that the proposed model explains 72% of the variance in overall satisfaction (Adjusted R² = 0.71). Among the five predictors, perceived inclusion emerged as the most influential factor (β = 0.64, p < 0.001), surpassing perceived accessibility (β = 0.18, p < 0.01) and intermodality (β = 0.11, p < 0.05). Safety and environmental conditions showed weaker but significant associations. These findings provide empirical evidence that inclusive perceptions—rather than merely physical or operational aspects—constitute the primary driver of satisfaction in urban railway systems. The study contributes to accessibility research by integrating psychosocial and perceptual dimensions into the evaluation of public transport performance. It also offers actionable implications for inclusive design, passenger communication, and service management strategies in metropolitan rail systems, particularly in Latin American contexts undergoing infrastructure expansion and modernization. Full article

Urban railway bridges are critical components of modern transportation networks. Dynamic loads and harsh environments put urban railway bridges at high risk of damage. Conventional vibration-based damage detection approaches often fail to provide sufficient accuracy and robustness under complex urban conditions. To address this limitation, this study introduces a hybrid deep learning framework that integrates a one-dimensional convolutional neural network (1D-CNN) and a recurrent neural network (RNN) for automatic damage detection using Linear Variable Differential Transformer (LVDT) displacement data. Start with the calibration of a finite element model (FEM) of the target bridge, achieved through updating the model parameters to align with field-acquired LVDT data, thereby establishing a robust and reliable baseline representation of the structure’s behaviour. Subsequently, a series of failure and damage scenarios is introduced within the FEM, and the associated dynamic displacement responses are generated to construct a comprehensive synthetic training dataset. These time-series responses serve as input for training a hybrid deep learning architecture, which integrates a one-dimensional convolutional neural network (1D-CNN) for automated feature extraction with a recurrent neural network (RNN) designed to capture the temporal dependencies inherent in the structural response data. Results show rapid convergence and minimal error in single-damage cases, and robust performance in multi-damage conditions on a dataset exceeding 5 million samples; the model attains a mean absolute error of ≈3.2% for damage severity and an average localisation error of <0.7 m. The findings highlight the effectiveness of combining numerical simulation with advanced data-driven approaches to provide a practical, data-efficient, and scalable solution for structural health monitoring in the urban railway context. Full article

High-speed railway turnouts play important roles in the efficient operation of trains. However, the complex mechanical structure of turnouts and insufficient displacement of switch rails under dynamic conditions create a point of vulnerability for high-speed railways. The insufficient displacement of switch rails in high-speed railway No. 18 turnouts critically impacts operational safety. This study establishes a coupled finite element model of the switch rail and sliding track bed plate to analyse the effects of the friction coefficient and wheel–rail force. The results show that without considering the force of the iron block, the maximum insufficient displacement of a switch rail occurs at sleeper No. 27, and the maximum insufficient displacement increases linearly with the friction coefficient, with a regression coefficient of 1.02. When considering the wheel–rail force of the train, the maximum insufficient displacement of the switch rail occurs at sleeper No. 25, with the regression coefficient reduced to 0.67. Through dynamic and static tests and a case analysis, the influence of wheel–rail force on the insufficient displacement of a switch rail is verified. The results show that the application of a lateral wheel–rail force in the model significantly reduces the insufficient displacement of the switch rail, with an improvement of more than 90%. This study can significantly improve the optimisation of turnout design and the operational efficiency of a railway network. Full article

Areas with well-developed networks of equestrian routes attract riders, contributing to tourism development and boosting the economy. As the most critical elements of equestrian routes are road, railway, and watercourse crossings, the construction of bridges and underpasses that meet equestrian needs is crucial. Due to the lack of clear, standardised guidance for the design of equestrian bridges and underpasses, a systematic literature review was conducted to identify and select manuals deal with equestrian bridge and/or underpass design. The selection criterion required that the manual be currently valid, written in English, and published online with open access, ensuring easy accessibility for engineers and policymakers. This paper summarises, compares, and comments on the design parameters of equestrian bridges and underpasses listed in the analysed manuals, which must be considered to achieve optimal solutions for both horse and rider. It also provides an overview of general recommendations and best practices for specific design parameters. In the absence of a manual offering comprehensive, standardised guidelines for the design of equestrian bridges and underpasses, this paper may assist policymakers, developers, and designers in creating a trail network suitable for equestrians. Full article

The railway architectural heritage in Jilin Province, as a significant component of Northeast China’s modern railway network, demonstrates how construction techniques, cultural integration, and social transformation have evolved throughout different historical periods. In this study, we conducted a systematic survey of 474 railway heritage buildings along the province’s main line. In order to quantitatively classify the spatiotemporal distribution characteristics of the heritage sites, we used five key Geographic Information System (GIS) methods—kernel density estimation, nearest neighbour index, spatial autocorrelation, standard deviational ellipses, and mean centre analysis—along with information entropy, relative richness, and the Bray–Curtis dissimilarity index. We continued our binary logistic regression using four prerequisite parameters—location, structure, architecture, and function—which contribute to the prerequisite, fundamental, and driving factors of architectural heritage. We concluded that local culture shapes geopolitics, population migration triggers economic conservation, and design trends carry ideology. These three factors intertwine to influence architecture and spatial patterns. Compared with previous studies, this research fills the gap concerning the architectural characteristics of towns at various lower-and mid-level stations, as well as the construction activities during the affiliated land period. This study provides a systematic framework for analysing railway heritage corridors and supports their sustainable conservation and reuse. Full article

Maritime logistics and railway logistics are crucial in cross-border logistics, and their integration forms a sea-rail cross-border intermodal logistics network. Against the backdrop of frequent unexpected events in today’s world, the normal operation of the sea-rail cross-border intermodal logistics network is under considerable threat. Therefore, researching the vulnerability of the intermodal network is extremely urgent. To this end, this paper first constructs a topological model of the sea-rail cross-border intermodal logistics network, designed to reflect the crucial process of “inter-layer transshipment” via transshipment nodes. Subsequently, a cascading failure model is developed to evaluate network vulnerability, featuring a load redistribution process that distinguishes between transshipment and non-transshipment nodes. The paper yields three primary findings. First, it identifies the optimal values for the capacity factor, overload factor, and inter-layer load transfer rate that most effectively mitigate the network’s vulnerability. Second, compared to a single sub-network (such as a maritime logistics network or a railway logistics network), the sea-rail cross-border intermodal network exhibits lower vulnerability when facing attacks. Third, it highlights the critical role of transshipment nodes, confirming that their failure will make the entire sea-rail cross-border intermodal logistics network more vulnerable. Full article

Anomaly detection of catenary support components (CSCs) is an important component in railway condition monitoring systems. However, because the abnormal features of CSCs loosening are not obvious, and the current CNN models and visual Transformer models have problems such as limited remote modeling capabilities and secondary computational complexity, it is difficult for existing deep learning anomaly detection methods to effectively exert their performance. The state space model (SSM) represented by Mamba is not only good at long-range modeling, but also maintains linear computational complexity. In this paper, using the state space model (SSM), we proposed a new visual state space reconstruction network (VSM-UNet) for the detection of CSC loosening anomalies. First, based on the structure of UNet, a visual state space block (VSS block) is introduced to capture extensive contextual information and multi-scale features, and an asymmetric encoder–decoder structure is constructed through patch merging operations and patch expanding operations. Secondly, the CBAM attention mechanism is introduced between the encoder–decoder structure to enhance the model’s ability to focus on key abnormal features. Finally, a stable abnormality score calculation module is designed using MLP to evaluate the degree of abnormality of components. The experiment shows that the VSM-UNet model, learning strategy and anomaly score calculation method proposed in this article are effective and reasonable, and have certain advantages. Specifically, the proposed method framework can achieve an AUROC of 0.986 and an FPS of 26.56 in the anomaly detection task of looseness on positioning clamp nuts, U-shaped hoop nuts, and cotton pins. Therefore, the method proposed in this article can be effectively applied to the detection of CSCs abnormalities. Full article

China’s high-speed railway (HSR) network relies heavily on bridge structures to ensure track regularity, with many lines crossing seismically active near-fault zones. Near-fault ground motions are characterized by significant vertical components (VGMs), which challenge conventional seismic design practices. Although seismic isolation techniques are widely adopted, the effects of VGMs on the dynamic response and damage mechanisms of HSR track–bridge systems remain insufficiently studied. To address this gap, this study develops a refined finite element model (FEM) in OpenSEES that integrates CRTS II slab ballastless tracks, bridge structures, and friction pendulum bearing (FPB). Using nonlinear time-history analyses, the research systematically investigates structural responses and damage degrees under different ratios of vertical-to-horizontal peak ground acceleration (α_VH) and multiple seismic intensity levels (frequent, design, and rare earthquakes). Key findings reveal that α_VH values in near-fault regions frequently range between 0.5 and 1.5, often exceeding current design code specifications. The impact of VGMs intensifies with seismic intensity: negligible under frequent earthquakes but significantly amplifying damage to piers, bearings, and track interlayer components (e.g., sliding layers and CA mortar layers) during design and rare earthquakes. While seismic isolation effectively mitigates structural responses through energy dissipation by bearings, it may increase sliding layer displacements and lead to bearing failure under rare earthquakes. Based on these insights, tiered α_VH values are recommended for seismic design: 0.65 for frequent, 0.9 for design, and 1.2 for rare earthquakes. These findings provide critical references for the seismic design of HSR infrastructure in near-fault regions. Full article

Railway service interruptions and electrical hazards often arise due to bird nests concealed within the intricate, highly symmetric overhead catenary networks of high-speed lines. These nests are difficult to pinpoint automatically, not only because they are diminutive and often merge visually with the surroundings but also due to occlusions and the persistent lack of substantial labeled datasets. To address this bottleneck, this work presents the High-Speed Railway Catenary Nest Dataset (HRC-Nest), merging 800 authentic images and 1000 synthetic samples to capture a spectrum of scenarios. Building on the symmetry of catenary structures—where nests appear as localized asymmetries—the Symmetry-Aware Railway Nest Detection Framework (RNDF) is proposed, an enhanced YOLOv12 system for accurate and robust nest detection in symmetric high-speed railway catenary environments. With the A2C2f_HRAMi design, the RNDF learns from multi-level features by unifying residual and hierarchical attention strategies. The SCSA component boosts the recognition in visually cluttered or obstructed settings further by jointly processing spatial and channel-wise signals. To sharpen the detection accuracy, particularly for subtle, hidden nests, the Focaler-GIoU loss guides bounding box optimization. Comparative studies show that the RNDF consistently outperforms recent detectors, surpassing the YOLOv12 baseline by 5.95% mAP@0.5 and 26.16% mAP@0.5:0.95, underscoring its suitability for symmetry-aware, real-world catenary anomaly monitoring. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

In this paper, the concept of symmetry is utilized to inform the structural design of our multi-sensor fusion framework—that is, the hierarchical feature extraction and spatial–temporal correlation modeling exhibit symmetrical properties across sensor nodes and temporal scales. Monitoring bearing temperature in high-speed train bogies is crucial for assessing system health and ensuring operational safety. Accurate temperature prediction facilitates proactive maintenance. However, existing models struggle to capture multi-scale temporal patterns, long-term dependencies, and spatial correlations among bearings, and they often overlook varying operating conditions. To address these challenges and enhance prediction accuracy in real-world operations, this study proposes MSC-Ada-MTL, a novel framework that integrates multi-scale feature extraction and operating condition recognition through adaptive multi-task learning. The approach employs multi-scale hierarchical temporal networks (MSHNets) to capture temporal features across different scales from multiple bogie sensors. A speed-based recognition strategy classifies operating conditions to enhance model reliability and simplify prediction tasks. By leveraging multi-task learning, the framework simultaneously models temporal dynamics and spatial correlations, creating a comprehensive prediction model. Validation and ablation experiments demonstrate significant improvements in prediction accuracy and robustness across diverse operating scenarios. The proposed method effectively addresses the limitations of existing approaches by synergistically combining temporal multi-scale analysis, operational condition awareness, and spatial–temporal relationship modeling, providing enhanced adaptability for real-world railway maintenance applications. Full article