This paper aims to offer a novel viewpoint for improving performance and reliability by developing and optimizing suspension components in a Y25 bogie through material optimization based on wheel-rail interactions under variable load and track conditions.

The suspension system, a critical component ensuring adaptation to road and load conditions in all vehicle types, is especially vital in heavy freight and passenger trains. In this context, the suspension set of the Y25 bogie - commonly used in Türkiye and Europe - was modelled using CATIAV5, and stress analyses have been performed by way of ANSYS using the finite element analysis (FEA) method. E300-520-M cast steel was selected for the bogie frame, while two different spring steels, 61SiCr7 and 51CrV4, were considered for the suspension springs. The modeled system was subjected to numerical analysis under loading conditions. The resulting stresses and displacements were compared with the mechanical properties of the selected materials to validate the design.

The results demonstrate that the mechanical strength and deformation characteristics of the suspension components vary according to the applied external loads. The stress and displacement responses of the system were found to be within the allowable limits of the selected materials, confirming the structural integrity and reliability of the design. The suspension set is deemed suitable for the prescribed material and environmental conditions, suggesting potential for practical application in real-world rail systems.

This research contributes to the design and optimization of bogie suspension systems using advanced CAD/CAE tools. It thinks that the material selection and numerical validation approach presented here can guide future designs in heavy load rail applications and potentially improve both safety and performance.

This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method, thereby realizing the systematic risk identification of regional hidden dangers.

The paper proposes a regional systematic risk identification method based on Bayesian and independent component analysis (ICA) theories. Firstly, the Gray Wolf Optimization (GWO) algorithm is used to partition each group of monitoring data in the hidden danger area, so that the data distribution characteristics within each sub-block are similar. Then, a distributed ICA early warning model is constructed to obtain prior knowledge such as control limits and statistics of the area under normal conditions. For the online evaluation process, the input data is partitioned following the above-mentioned procedure and the ICA statistics of each sub-block are calculated. The Bayesian method is applied to fuse online parameters with offline parameters, yielding statistics under a specific confidence interval. These statistics are then compared with the control limits - specifically, checking whether they exceed the pre-set confidence parameters - thus realizing the systematic risk identification of the hidden danger area.

Through simulation experiments, the proposed method can integrate prior knowledge such as control limits and statistics to effectively determine the overall stability status of the area, thereby realizing the systematic risk identification of the hidden danger area.

The proposed method leverages Bayesian theory to fuse online process parameters with offline parameters and further compares them with confidence parameters, thereby effectively enhancing the utilization efficiency of monitoring data and the robustness of the analytical model.

Severe scarcity of natural river sand (RS), exacerbated by environmental protection policies and extraction constraints, has significantly impacted aggregate supply for railway concrete. While manufactured sand (MS) offers a substitute for RS in railway applications, its widespread adoption in high-strength railway prestressed structures is challenged by lack of drying shrinkage and creep research data on concrete.

High-strength manufactured sand concrete (MSC) was prepared using MS with varying lithologies and stone powder contents. Its drying shrinkage and creep behaviors were evaluated in accordance with the Chinese standard GB/T 50082. The deformation mechanism was analyzed by combining nano-scratch testing.

Compared to RS concrete, MSC from all tested lithologies showed higher drying shrinkage but lower creep deformation. The drying shrinkage rose steadily with increased stone powder content, while the creep strain displayed a distinct non-linear trend, decreasing first before rising. To prepare low-deformation MSC, select high-strength MS and limit stone powder content not greater 10%. Nano-scratch tests indicated that harder MS particles suppress microcracking at the interfacial transition zone (ITZ), improving the creep resistance. The predictive models for drying shrinkage and creep were also developed by incorporating coefficients for stone powder and lithology effects.

These findings serve as a foundation for the application of MSC in railway prestressed structures, offering both theoretical and practical guidance.

In recent years, the rapid advancement of artificial intelligence (AI) has exerted profound impacts on and provided strong impetus to numerous fields in the industrial sector. Within the railway industry, AI has driven continuous upgrading and optimization of intelligent train control technology, thanks to its enhanced computational capabilities derived from advanced algorithms and models, as well as its role in improving safety performance. Integrating AI technology more extensively into train autonomous driving and control has thus become an inevitable trend in the global development of railways.

This paper, therefore, conducts a comprehensive analysis of the development progress and current status of AI technology applications in the field of train driving and control on a global scale. It systematically sorts out and analyzes the advantages of various AI technologies and the positive impacts they bring to the upgrading of train control technology, elucidates the feasibility and future prospects of applying a range of emerging AI technologies from the perspective of technical theory and provides guidance for the intelligent development of this field from a practical perspective.

The application of AI technology in the train driving and control field is still in its infancy. While a large number of AI technologies have been widely adopted, there remains significant room for further optimization and improvement of these technologies. Additionally, a variety of AI technologies that have been applied in other industrial sectors but not yet widely implemented in training autonomous driving and control have demonstrated tremendous development potential.

The research findings provide references and guidance for advancing train control technology, promoting the digital transformation of railways, accelerating the overall optimization and upgrading of railway industry technologies, and facilitating the accelerated development of global railways.

This research aims to monitor seismic intensity along railway lines, study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribution along railway routes, thereby achieving graded post-earthquake response measures.

The seismic intensity monitoring system for railways adopts a two-level architecture, namely the seismic intensity monitoring equipment and the seismic intensity rapid reporting information center processing platform. The platform obtains measured instrumental intensity through the seismic intensity monitoring equipment deployed along railways and combines it with the National Seismic Network Earthquake Catalog to generate real-time railway seismic intensity distribution maps using the Kriging interpolation algorithm. A calculation method for railway seismic impact intervals is designed to calculate the mileage intervals where the intensity area corresponding to each contour line in the seismic intensity distribution map intersects with the railway line.

The system was deployed for practical earthquake monitoring demonstration applications on the Nanjiang Railway Line in Xinjiang. During the operational period, the seismic intensity monitoring equipment calculated and uploaded instrumental intensity values to the seismic intensity rapid reporting information center processing platform a total of nine times. Among these, earthquakes triggering the Kriging interpolation algorithm occurred twice. The system operated stably throughout the application period and successfully visualized relevant seismic impact data, such as earthquake intensity distribution maps and affected railway mileage sections. These results validate the system's practicality and effectiveness.

The seismic intensity monitoring for the railway system designed in this study can integrate the measured instrumental intensity data along railways and the earthquake catalog of the National Seismic Network. It uses the Kriging interpolation method to calculate the intensity distribution and determine the seismic impact scope, thereby addressing the issue that the seismic intensity distribution calculated by traditional attenuation formulas deviates from reality. The system can provide clear graded interval recommendations for post-earthquake disposal, effectively improve the efficiency of post-earthquake recovery and inspection and offer a decision-making basis for restoring railway operations quickly.

This paper investigates how high-speed rail (HSR) influences socioeconomic inequality by providing the first systematic bibliometric review of research trends, methodological approaches and thematic structures. It examines whether HSR fosters balanced regional development or reinforces spatial disparities.

Using the Bibliometrix R package, 237 records were retrieved from the Web of Science (1985-2024). Citation indicators, keyword co-occurrence and collaboration networks were combined with natural language processing (NLP) to classify studies by territorial scale, methodology, economic variables and inequality outcomes.

The paper offers the first structured overview of how the literature conceptualizes the link between HSR and inequality. It highlights persistent gaps - scarcity of city-level analyses, limited socioeconomic indicators and reliance on Chinese case studies - providing a foundation for more comparative and interdisciplinary research.

This paper contributes by offering a structured overview of how the literature has conceptualized and measured the relationship between HSR and inequality. By identifying persistent research gaps - such as the scarcity of city-level analyses, limited use of socioeconomic indicators, and overreliance on Chinese case studies - it provides a foundation for more comparative and interdisciplinary approaches. The study informs policymakers and researchers on how to design future infrastructure projects that balance efficiency with equity.

This paper aims to systematically review the evolution of inspection technologies and equipment for heavy-haul railway infrastructure, with a focus on China's Shuohuang Railway and Daqin Railway. It summarizes the technological progression from traditional manual inspections to integrated and intelligent inspection systems, analyzes their practical application outcomes and outlines future research directions to support the safe, efficient and sustainable operation of heavy-haul railways.

The study employs a combination of historical and empirical analysis, primarily drawing on academic literature and operational data from Shuohuang Railway. The development of inspection technologies is categorized into two distinct phases: traditional inspection and integrated inspection. The comprehensive effectiveness of these technologies is evaluated based on actual inspection efficiency, defect detection capability, cost savings and other relevant data.

The adoption of integrated inspection vehicles has significantly improved inspection efficiency and accuracy. In 2014, the world's first heavy-haul integrated inspection vehicle enabled synchronous multidisciplinary inspections, greatly reducing reliance on manual labor. By 2024, the intelligent heavy-haul integrated inspection vehicle further enhanced detection precision by 30%. Practical applications demonstrate that the annual number of track defects decreased from 25,000 to 3,800, while the track quality index (TQI) remained stable below 6 mm. Additionally, annual maintenance costs were reduced by more than 40 m yuan.

This paper provides the first systematic review of the development of inspection technologies for heavy-haul railway infrastructure, highlighting China's leading achievements in integrated and intelligent inspection. It clarifies the practical value of these technologies in enhancing safety, reducing costs and optimizing maintenance operations. Furthermore, it proposes future directions for development, including system integration, onboard computing capabilities and unmanned operations, offering valuable insights for technological innovation and policymaking in the field.

This study aims to enhance the accuracy of key entity extraction from railway accident report texts and address challenges such as complex domain-specific semantics, data sparsity and strong inter-sentence semantic dependencies. A robust entity extraction method tailored for accident texts is proposed.

This method is implemented through a dual-branch multi-task mutual learning model named R-MLP, which jointly performs entity recognition and accident phase classification. The model leverages a shared BERT encoder to extract contextual features and incorporates a sentence span indexing module to align feature granularity. A cross-task mutual learning mechanism is also introduced to strengthen semantic representation.

R-MLP effectively mitigates the impact of semantic complexity and data sparsity in domain entities and enhances the model's ability to capture inter-sentence semantic dependencies. Experimental results show that R-MLP achieves a maximum F1-score of 0.736 in extracting six types of key railway accident entities, significantly outperforming baseline models such as RoBERTa and MacBERT.

This demonstrates the proposed method's superior generalization and accuracy in domain-specific entity extraction tasks, confirming its effectiveness and practical value.

Type-120 relief valves are critical components of locomotive braking systems, and they rapidly discharge the air pressure during brake release to enable swift pressure relief. In order to develop type-120 relief valve rubber diaphragms with long life and high performance, the damaged faulty samples were analyzed and studied.

Finite element analysis (FEA) was used to investigate the stress distribution and failure mechanism of the rubber diaphragms within the type-120 relief valves under dynamic loading conditions. The Ogden hyperelastic constitutive model was used to fit the diaphragm data obtained from the uniaxial tensile tests, and its suitability for the modeling of large deformations was confirmed.

The FEA results indicated that, when the rubber diaphragms reached their maximum deformation, the peak stress on their upper surfaces was 5.44 MPa. Thus, this region is highly susceptible to fatigue damage. The service life of the rubber diaphragms could be extended by using rubber compounds with high tensile moduli or a fabric-reinforced rubber diaphragm.

This study provides valuable data and experience for the development of the rubber diaphragms in the type-120 valves and other long-life rubber products in the railway field.

Regarding that Ultraviolet radiation, pollutant adsorption, and environmental changes may be the main reasons for the aging and yellowing on windshield rubber in high-speed trains, countermeasures are proposed to solve the aging and yellowing of windshield rubber and reduce the adverse effects caused by rubber yellowing.

Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) were used to test the yellowed windshield rubber. Aging tests, including UVaging, natural aging and salt spray aging, were conducted to analyze the effects of aging on the windshield rubber. Different cleaning agents were selected to soak the windshield rubber, and the quality, hardness, and surface appearance of the rubber samples were tested.

After UV aging, antioxidants migrated to the surface of the windshield rubber, but due to oxidation failure, they could not capture free radicals, leading to continued oxidation reactions within the material and resulting in yellowing of the rubber in a short period of time.

Cleaning agents have a minimal impact on windshield rubber, UV aging has the greatest impact and natural aging is a gradual and slow deterioration process. Through daily deep cleaning and maintenance with protective agents at regular intervals, the deterioration of windshield rubber yellowing in high-speed trains can be effectively suppressed.