For the commonly used concrete mix for railway tunnel linings, concrete model specimens were made, and springback and core drilling tests were conducted at different ages. The springback strength was measured to the compressive strength of the core sample with a diameter of 100mm and a height-to-diameter ratio of 1:1. By comparing the measured strength values, the relationship between the measured values under different strength measurement methods was analyzed.

A comparative test of the core drilling method and the rebound method was conducted on the side walls of tunnel linings in some under-construction railways to study the feasibility of the rebound method in engineering quality supervision and inspection.

Tests showed that the rebound strength was positively correlated with the core drill strength. The core drill test strength was significantly higher than the rebound test strength, and the strength still increased after 56 days of age. The rebound method is suitable for the general survey of concrete strength during the construction process and is not suitable for direct supervision and inspection.

By studying the correlation of test strength of tunnel lining concrete using two methods, the differences in test results of different methods are proposed to provide a reference for the test and evaluation of tunnel lining strength in railway engineering.

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 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.

To address the encapsulation challenge of fiber Bragg grating (FBG) sensors in complex railway environments, this paper designs a clip-on composite sensor enabling installation-friendly deployment and long-term axle counting system monitoring.

Wheel-rail mechanical behavior was simulated via finite element analysis (FEA) to determine optimal sensor placement. A clip-on composite sensor was subsequently engineered. Stress transduction efficacy was validated through FEA quantification of stress responses at the axle counter location. Findings - The proposed FBG axle counter integrates temperature compensation and anti-detachment monitoring as well as advantages such as simplified installation with minimal maintenance and sustained operational reliability. It effectively transmits stress, yielding a measured strain of 39 μe under static loading conditions without sensitivity-enhancing elements.

This study performs FEA of wheel-rail stress distribution and engineers the dual-slot composite sensor, FEAwas conducted to quantify the stress magnitude at the axle sensor position of the dual-slot composite sensor. Additionally, FEA was performed on sensors with different structural configurations, including adjustments to the axle sensor position, number of slots and axle position. The results confirmed that the designed composite sensor exhibits superior stress transfer characteristics.

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.

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.

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.

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.

Weathering steel has excellent resistance to atmospheric corrosion, but still faces complex environmental corrosion problems during long-term operation. This paper mainly studies the corrosion problem of weather resistant steel materials for railway freight car bodies with a load capacity of 70 tons.

The paper analyzes the corrosion characteristics of weather resistant steel materials for truck bodies through macroscopic and microscopic methods including metallographic microscopy, scanning electron microscopy, energy dispersive spectroscopy and X-ray diffraction. Electrochemical analysis shows that the rust layer on the surface of weathering steel changes the surface state of the material, and also proves that weathering steel used in trucks undergoes electrochemical corrosion under atmospheric corrosion. At the same time, ion chromatography technology is used to study the corrosive ions mainly present in the residual liquid and foam solution inside the vehicle body.

The corrosion of truck body materials is mainly electrochemical corrosion, and the corrosion of door materials is more obvious than that of other parts. The corrosion products are mainly Fe oxides and hydroxides. There are high concentrations of Cl^- and SO42^- ions in the residual liquid and foam solution at the bottom of the freight car, which are the main factors causing corrosion of the railway freight car body.

The foam adhesive around the door panel is in a moist state for a long time, and corrosive ions will accelerate the electrochemical corrosion of the weather resistant steel material of the door panel. Therefore, the corrosion of the cargo door panel is more severe than other components.

This study examines the effect of increased surface energy on adhesion strength. Surface modifications were made using chemical coating methods such as primer paint (primer) and cataphoresis (KTL, Kathodische Tauchlackierung). The wetting behaviour of adhesive on these surfaces and the resulting contact angles were analysed to evaluate bonding effectiveness.

Primer paint was applied to glass fibre reinforced plastic (GFRP) materials and cataphoresis coating was applied to steel. Contact angles of the coated surfaces were measured and compared to those of the uncoated (natural) surfaces.

Results showed that applying primer to GFRP and KTL to steel increased their surface energy compared to untreated surfaces. A decrease in contact angle correlated with improved wetting, suggesting enhanced adhesion potential.

While the effects of surface coatings on adhesion have been studied, there is limited research specifically on the adhesion-enhancing potential of KTL coatings. Typically used for corrosion resistance, KTL is shown here to also improve adhesion. The novelty lies in experimentally demonstrating KTL's dual role as both a protective and adhesion-enhancing layer.