This paper presents an integrated technical and economic assessment of hot axle box detectors (HABD) and hot wheel detectors (HWD) important components of derailment prevention strategies, whose role extends beyond auxiliary monitoring to become cornerstones of derailment prevention and operational resilience. Building upon the International Union of Railways (UIC) Network Monitor Phases 1–3, the study synthesises international benchmarking evidence, cost–benefit analysis and operational practice to provide a coherent framework for decision-making, deployment and harmonisation.

The methodology combines three layers: (1) international benchmarking of national rules, detector spacing and alarm thresholds across Europe, North America, Asia and Australia; (2) a cost–benefit model based on avoided accidents, fatalities and disruption, tested through sensitivity analyses of detector density and false-alarm rates; and (3) operational doctrines covering alarm logic, reliability, availability, maintainability and safety requirements, operations control centre integration and lifecycle maintenance. The findings are aligned with European Union Agency for Railways guidance, UIC International Railway Solution (IRS) 70729 and lessons from Demonstration of Railway Infrastructure Reliability and Sustainable Railway projects.

Findings confirm that HABD/HWD systems provide significant safety and economic value when integrated into operational rulebooks, maintenance workflows and digital platforms. Multi-sensor and AIenabled detectors reduce false positives and enhance early detection, while international harmonisation efforts (e.g. IRS 70729 and EHMS) support cross-border interoperability. This study provides evidence-based guidance for risk-based deployment and lifecycle optimisation within modern railway safety frameworks.

This paper provides a comprehensive international synthesis that connects benchmarking, cost–benefit and operational safety frameworks into a structured doctrine for HABD/HWD deployment. It provides infrastructure managers and railway undertakings with evidence-based guidance on risk-based siting, lifecycle optimisation and international harmonisation. The work also contributes to the forthcoming UIC IRS, positioning HABD/HWD as globally relevant digital safety assets within the broader agenda of railway innovation, digitalisation and socio-economic resilience.

The purpose of this study is to reduce wheel–rail vibration noise (with the noise level increasing by approximately 9 dB for every doubling of train speed) by enhancing wheel damping. Besides, it verifies the performance of the damping wheel and provides support for the engineering application of low-noise wheels.

This study takes the damping ring-constraint layer composite wheel as the research object. First, it proposes a wheel scheme combining a damping ring and constrained damping. Then, it verifies the natural frequency and damping of the proposed wheel via 3D finite element modeling and modal analysis. Finally, in the laboratory, the wheel–rail relationship test setup is used to conduct tests on two types of wheel structures (nondamping wheel and damping wheel) under radial and axial excitation.

The damping wheel significantly reduces the corresponding radiated sound power level, with an overall noise reduction of approximately 10 dB or more, especially in the high-frequency region (around 3,150 Hz). The damping ring reduces high-frequency noise, while the constraint layer suppresses medium-low frequency noise. The combined structure outperforms single-component structures in the full frequency range, as it can suppress both high-frequency whistling noise and medium-low rolling noise.

The originality of this study lies in proposing a wheel scheme that combines a damping ring and constrained damping. The study’s value is to provide a theoretical basis and technical guidance for the engineering application of low-noise wheels in rail vehicles.

Rail freight is widely recognized for its economic and environmental advantages, yet it remains weakly integrated into firms’ supply chains, particularly in emerging economies. This study aims to investigate the conditions under which rail freight can be effectively integrated into multi-actor supply chains, with specific attention to the role of organizational coordination, information quality and artificial intelligence (AI) in shaping logistics integration outcomes.

The study draws on a quantitative survey of 3,185 stakeholders involved in rail-based and multimodal supply chains in Morocco. The data are analyzed using a combination of machine learning, deep learning and artificial neural network models. These methods are used not only to identify the main determinants of rail freight integration but also to capture non-linear relationships, interaction effects and potential integration trajectories that cannot be addressed through conventional linear models.

The results show that rail freight integration depends primarily on organizational and informational mechanisms rather than on infrastructure alone. Inter-organizational coordination and logistics information quality emerge as the most influential factors. AI contributes positively to rail freight integration, but its effect is conditional: AI tools significantly enhance integration only when adequate levels of coordination and information sharing are already in place. Scenario simulations further reveal that the strongest integration gains arise from the combined improvement of organizational practices and AI adoption.

This research contributes to the literature by shifting the focus from infrastructure-centered explanations toward a systemic understanding of rail freight integration. It is among the first studies to empirically combine machine learning, deep learning and artificial neural networks to analyze logistics integration in an emerging-economy context and to show that AI functions as a complementary and amplifying mechanism rather than a standalone solution.

The purpose of this study is to examine the determinants of modal shift intention from road freight transport to rail freight in Morocco, focusing on the perceived economic, energy and environmental performance of rail freight.

A quantitative survey was conducted among key freight transport stakeholders, including road carriers, industrial shippers, logistics operators and experts. A total of 483 valid questionnaires were collected. Measurement scales were derived from the literature and adapted to the Moroccan context. Data were analyzed using SPSS through descriptive statistics, reliability analysis, correlation tests and multiple linear regression.

The results indicate generally positive perceptions of rail freight, particularly regarding energy efficiency and environmental performance. All three perceived performance dimensions have a positive and significant effect on modal shift intention. Perceived energy performance emerges as the strongest predictor, followed by environmental impact, while economic performance shows a significant but more moderate influence. The model demonstrates strong explanatory power. Research limitations/implications – The study relies on perceptual data and a non-probabilistic sampling approach, which may limit the generalizability of the findings. Future research could integrate objective cost, energy and emission data; apply longitudinal designs or extend the model by incorporating institutional, infrastructural and policy-related variables to further explain rail freight adoption. Practical implications – The findings provide valuable insights for policymakers, rail operators and logistics managers by highlighting the key levers for promoting rail freight development. Strengthening rail energy efficiency, improving service reliability and enhancing intermodal integration can significantly increase stakeholders’ willingness to shift freight from road to rail. Social implications – By encouraging modal shift toward rail freight, the study supports broader societal objectives related to environmental protection, energy security and sustainable development. Increased use of rail freight can contribute to reduced greenhouse gas emissions, lower road congestion and improved quality of life in urban and industrial areas.

This study provides one of the first empirical investigations of rail freight modal shift determinants in Morocco, offering an integrated analysis of economic, energy and environmental factors within a single conceptual framework.

There are significant differences in the corrosion protection performance of commonly used cleaning agents for high-speed railways. In order to study the dual requirements of cleaning efficiency and corrosion inhibition, explore the differences in corrosion protection performance of cleaning agents, effectively protect metal substrates, and ensure the safe, economical, and environmentally friendly operation of high-speed railways, this study is hereby carried out.

This study investigated the corrosion behaviour of Q235 steel exposed to acidic, neutral, and alkaline cleaning agents through metallographic analysis, electrochemical testing, and scanning electron microscopy (SEM).

Electrochemical behaviour was assessed at concentrations of 5%, 10%, 15%, and 20% using electrochemical impedance spectroscopy (EIS) and Tafel tests. The results indicate that the protective efficacy of acidic, neutral, and alkaline cleaning agents follows the order: alkaline > neutral > acidic.

Microstructural analysis and energy-dispersive X-ray spectroscopy (EDS) surface element quantification reveal that acidic cleaning agents cause the most severe corrosion and offer the poorest protection for Q235 steel, whereas neutral and alkaline agents provide protective effects by retarding corrosion. Conducting in-depth research on the differences in corrosion protection performance of cleaning agents can effectively safeguard metal substrates, eliminate corrosion risks, and ensure the safe operation of high-speed railways.

To support the operational safety and lightning protection design of high-speed maglev railways, this paper quantitatively evaluates how suspension height and operating speed influence lightning susceptibility. It characterizes trends of the critical background electric field with respect to these two variables, tracks the evolution of surface hotspot distributions and identifies dominant attachment locations and their sensitivity.

A coupled procedure of “electrostatic field–aerodynamic flow field–scaled assessment” is proposed. The electrostatic model provides surface field-enhancement factors and their spatial distribution, while turbulent-flow simulations characterize near-wall density variations induced by speed. Under a unified leader height, a critical criterion based on a density-scaled breakdown field maps these two fields to a train-wise critical background electric field. Representative regions (nose, roof and bottom or tail) are used to build statistical metrics for hotspot migration and dominance with speed.

Increasing suspension height weakens electric-field coupling to ground, raises the critical background-field threshold and reduces the relative contribution of bottom and edge regions. At the same suspension height, a rigidly grounded train has a lower critical threshold than an electrically floating one. Within 0–500 km/h, the train-wise threshold decreases slowly with speed. Region-wise, roof-tail and bottom-mid sections show a decreasing trend with speed, while the nose stagnation point increases slightly; over the entire speed range, the dominant region remains the roof-tail section.

Within a unified framework, suspension height and operating speed affect lightning attraction through two distinct channels. Suspension height mainly modifies the threshold and hotspot distribution by changing geometric polarization, whereas speed alters discharge-initiation difficulty through aerodynamically induced density variations. The framework evaluates these effects separately and in combination, explaining the slow variation of the global threshold and the subtle evolution of hotspot locations and providing a physics-based reference for lightning protection design and operational safety assessment of high-speed maglev railway systems.

With the development of railway systems towards intelligence, informatization and networking, their architecture design becomes increasingly complex. Traditional safety analysis methods (such as failure mode and effects analysis (FMEA), fault tree analysis (FTA) and event tree analysis) can no longer realise integrated safety analysis across disciplines, domains and life cycles amid requirement drift, architecture iteration and operational scenario evolution. This paper aims to introduce a systematic, integrated, model-driven safety analysis framework for the entire life cycle of railway systems to address these complex safety challenges and improve the overall safety level of railway systems.

First, the paper conducts a literature review of traditional railway safety analysis techniques and their applications, and analyzes the technical framework, core elements (modelling languages, methods, and tools), and advantages of Model-Based Systems Engineering (MBSE). Then, it studies the integration of MBSE and system safety analysis, focusing on typical international research cases (e.g., the Methodology for the Description and Safety Analysis of Interoperable Systems (MeDISIS), the European Train Control System (ETCS) safety verification project SafeSysE, and the Reference Architecture for Model-Based System and Software Engineering in the Railway Domain (RAMSAS), etc.) and domestic research progress, and summarizes the core idea of integrating MBSE with safety analysis in the design process. Finally, it explores the key technologies of MBSE-based railway system safety analysis, including automatic mapping of architecture models to Fault Tree Analysis (FTA), dynamic linkage between behaviour models and Failure Mode and Effects Analysis (FMEA), multi-model collaboration and dynamic update, as well as technologies in three aspects: safety requirement analysis driven by railway operational tasks, integrated safety-function design analysis, and simulation-based safety verification via train-fleet operation modelling. The development and validation platform Platform for Integrated Systems and Mechatronic Engineering (PRISME) and tools such as the Dependability Engineering and Innovation System (DEIS), Behavior-Driven Development (BDD) frameworks, and International Business Machines (IBM) engineering suites were also utilized to support this research.

The MBSE-based railway system safety analysis technique embeds safety activities into the forward-engineering workflow of MBSE-driven development, enabling concurrent safety and functional design. It solves the problems of model heterogeneity, data silos and process discontinuities in traditional safety analysis and realises end-to-end traceability and consistency from system requirements to safety analysis results. This technique not only provides a rigorous foundation for standardised, efficient and accurate safety assessment of railway systems but also offers technical support for early identification of potential safety issues, reduction of late-stage design changes and continuous optimisation of system safety performance.

The innovation of this paper mainly includes three aspects:(1) It breaks the limitations of traditional document-driven safety analysis methods, constructs an MBSE-based integrated safety analysis framework covering the entire life cycle of railway systems and turns safety work from an ad-hoc add-on into a systematic, goal-oriented activity. (2) It proposes key integration technologies such as automatic mapping of SysML-based architecture models to FTA, dynamic linkage between behaviour models (state machine diagram/activity diagram) and FMEA and multi-model (FTA/FMEA/hazard and operability analysis) collaborative dynamic update, which guarantee the consistency and traceability of safety analysis data and improve the efficiency of safety analysis iteration. (3) It develops a set of MBSE-based railway safety analysis implementation paths, including task-driven safety requirement decomposition, integrated safety function failure propagation modelling and train-fleet operation simulation-based verification, providing a practical technical solution for the safety design and analysis of complex railway systems.

The rapid expansion of high-speed railway (HSR) networks in Western China has increased the exposure of linear infrastructure to active faults. This study establishes and applies a probabilistic fault displacement hazard analysis (PFDHA) framework to quantify both on-fault surface rupture and distributed offfault permanent ground deformation (PGD) hazards for HSR crossings of the Xiaojiang Fault Zone (XJFZ).

A PFDHA framework is developed, integrating a Poissonian seismicity model with spatial rupture randomness. The methodology is applied to the XJFZ, which crosses the Nanning- Kunming (NK) and Shanghai-Kunming (SK) HSR. Permanent displacement hazards are evaluated for 2 probability levels: 10% and 2% probability of exceedance in 50 years.

Through the evaluation of displacement hazards for 50-year exceedance probabilities of 10% and 2%, this study finds that for the NK and SK HSR, permanent displacements at a 10% probability of exceedance range from 1.0 m to 2.9 m, peaking at fault intersections. Comparative analysis shows that traditional deterministic estimates (1.3–2.0 m) generally align with the probabilistic results but fail to capture the full range of risk.

This work adapts PFDHA to linear infrastructure in a tectonically active region of China, explicitly considering both on- and off-fault displacement within engineering-relevant corridors. The integration of regional rupture scaling and segment-based constraints provides a reproducible basis for displacement hazard assessment in HSR planning and retrofit.