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Applied Sciences
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Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance: 2nd Edition
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Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 5875

Editors

Dr. Jie Geng

E-Mail Website
Guest Editor
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Interests: reliability engineering; maintenance; maintainability analysis; virtual maintenance; maintenance strategy
Special Issues, Collections and Topics in MDPI journals
Dr. Yuntai Zhang

E-Mail Website
Guest Editor
1. School of Civil Engineering, Central South University, Changsha 410075, China
2. Department of Civil and Environmental Engineering, University of Macau, Taipa, Macau
Interests: AI-powered structural dynamics; seismic-resistant design; structural health monitoring
Dr. Zhipeng Lai

E-Mail Website
Guest Editor
School of Civil Engineering, Central South University, Changsha 410075, China
Interests: railway dynamics; seismic design; performance-based engineering

Special Issue Information

Dear Colleagues,

The global railway industry, including high-speed networks, remains a key driver of economic development and regional connectivity. As railway systems continue to expand, their operational complexity has significantly increased, posing ongoing challenges for infrastructure health monitoring, assessment, and maintenance. Ensuring the safety, reliability, and long-term sustainability of railway operations requires continuous advancements in these areas.

Following the success of the first edition, the second edition of this Special Issue focuses on the latest innovations in railway infrastructure management. Recent progress in sensing technologies, artificial intelligence (AI), and big data analytics has enabled more efficient monitoring, predictive maintenance, and data-driven decision-making. Additionally, emerging approaches such as digital twins, machine learning, and IoT-based systems offer new opportunities to enhance the resilience and sustainability of railway networks.

We welcome original research, case studies, and comprehensive review papers that address (but are not limited to) advanced monitoring techniques, AI-powered predictive maintenance, risk assessment and adaptation strategies, and lifecycle management and optimization. This Special Issue aims to foster interdisciplinary collaboration and showcase transformative solutions for the future of railway sustainability. Contributions from both academia and industry are highly encouraged.

Dr. Jie Geng
Dr. Yuntai Zhang
Dr. Zhipeng Lai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • railway infrastructure
  • health monitoring
  • artificial intelligence
  • digital twins
  • big data analytics
  • IOT-based systems
  • risk assessment
  • sustainable railway operations

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Related Special Issue

Published Papers (5 papers)

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Research

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22 pages, 2430 KB  
Article
Multimodal Data Analysis for Railway Switch Monitoring: Point Machine Motor Currents, Track Geometry, and Maintenance Records
by Susanne Reetz, Thorsten Neumann, Jörn Groos, Douwe Buursma and Wessel Roodenburg
Appl. Sci. 2026, 16(15), 7531; https://doi.org/10.3390/app16157531 - 29 Jul 2026
Viewed by 311
Abstract
Condition monitoring is an important tool in the maintenance of railway infrastructure, and data-driven approaches are gaining in popularity. Switches are complex systems that include both complex track superstructure—which is by design subject to increased dynamic load compared to open track—and point machines [...] Read more.
Condition monitoring is an important tool in the maintenance of railway infrastructure, and data-driven approaches are gaining in popularity. Switches are complex systems that include both complex track superstructure—which is by design subject to increased dynamic load compared to open track—and point machines that must operate in concert to achieve optimal performance. Existing condition monitoring methods are often limited to a specific type of measurement data to monitor one of these subsystems of a switch and only certain faults. Research and industry are now moving toward integrating all available information into a single, unified system, enabling a clearer understanding of the interactions between subsystems, more efficient maintenance, and better fault root cause diagnosis. This paper investigates the relationships between motor current curves of electric point machines, track geometry data collected by measurement trains, and maintenance logs for 58 switches in the Netherlands over almost 3.5 years. The aim is to provide a foundation for the design of such holistic monitoring systems as well to derive insights and requirements for future data gathering. The analysis reveals that maintenance actions are the dominant factor influencing the measurement data. Major track geometric modifications appear to act as persistent boundary conditions for point machine operation. Given that switches require more frequent maintenance than open track and point machines exhibit dynamic behavior, continuous track geometry monitoring during operation is essential for accurate data analysis and maintenance assessment. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance: 2nd Edition)
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19 pages, 2542 KB  
Article
State Evaluation of Wheel–Rail Force in High-Speed Railway Turnouts Based on Multivariate Analysis and Unsupervised Clustering
by Jiahui Wang, Tao Shen, Liang Huo, Yaoyao Wang and Hangyuan Qin
Appl. Sci. 2025, 15(22), 11934; https://doi.org/10.3390/app152211934 - 10 Nov 2025
Viewed by 1252
Abstract
The assessment of wheel–rail force states is a key technical issue in the safety monitoring of high-speed railway turnouts. Due to the complex geometry and severe load fluctuations of turnouts, wheel–rail interactions exhibit strong nonlinearity, asymmetry, and multidimensional coupling characteristics. Traditional methods suffer [...] Read more.
The assessment of wheel–rail force states is a key technical issue in the safety monitoring of high-speed railway turnouts. Due to the complex geometry and severe load fluctuations of turnouts, wheel–rail interactions exhibit strong nonlinearity, asymmetry, and multidimensional coupling characteristics. Traditional methods suffer from limitations such as reliance on labeled samples and poor real-time performance. This study proposes an intelligent evaluation method that integrates multivariate statistical analysis with unsupervised clustering, and establishes a multidimensional analytical framework incorporating data preprocessing, time-domain analysis, safety index evaluation, frequency-domain feature extraction, and cluster-based recognition. Using a turnout section of the Beijing–Tianjin Intercity Railway as a case study, four fundamental wheel–rail force components were selected as feature variables to reveal their dynamic patterns. The DBSCAN density-based clustering algorithm was employed to achieve unsupervised state identification, successfully classifying three typical operating states: normal, high-load abnormal, and extreme load. The clustering silhouette coefficient reached 0.563, significantly outperforming K-means and hierarchical clustering. Safety evaluation results indicate that all relevant indicators meet international standards. The proposed method requires no labeled samples and offers strong physical interpretability and engineering applicability, providing effective support for turnout condition awareness and predictive maintenance. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance: 2nd Edition)
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19 pages, 5138 KB  
Article
Judgment Method for Maintenance Accessibility Based on Human Visual Range in Virtual Environment
by Jie Geng, Shuyi Liu, Bingyi Liu, Zhuoying Gao, Ziyue Guo and Ying Li
Appl. Sci. 2025, 15(22), 11861; https://doi.org/10.3390/app152211861 - 7 Nov 2025
Viewed by 796
Abstract
Visibility and accessibility are two key elements in the qualitative analysis of maintainability and cover most work of such qualitative analysis. At present, visibility and accessibility are analyzed by virtual maintenance technology, which greatly improves the efficiency of maintainability analysis. Generally, in the [...] Read more.
Visibility and accessibility are two key elements in the qualitative analysis of maintainability and cover most work of such qualitative analysis. At present, visibility and accessibility are analyzed by virtual maintenance technology, which greatly improves the efficiency of maintainability analysis. Generally, in the maintainability analysis based on virtual maintenance, in order to analyze the visibility and accessibility, different analysis tools are established based on human visual features and arm motion features, respectively, for independent analysis. However, in actual maintenance, visibility and accessibility are simultaneously required to better complete maintenance. Therefore, judging whether the object is accessible while it is visible is obviously more efficient than calling different tools to analyze visibility and accessibility, and can better fit engineering practices. In this paper, the quantitative correlation between the optimal human visual range and the maximum accessible range was established by introducing auxiliary objects in the virtual environment based on the basic physiological characteristics of human visibility and accessibility. Whether the object is accessible was judged while it was within the optimal human visual range on the basis of this quantitative correlation. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance: 2nd Edition)
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18 pages, 4783 KB  
Article
Balancing Efficiency and Cost: A Technical and Economic Analysis of Condensed Maintenance
by Jan Schatzl and Stefan Marschnig
Appl. Sci. 2025, 15(21), 11688; https://doi.org/10.3390/app152111688 - 31 Oct 2025
Cited by 1 | Viewed by 862
Abstract
In Europe’s changing transport landscape, railways are experiencing a renaissance, driven by environmental advantages, cost efficiency, growing demand, and political support. Yet this growth also exposes major challenges, especially regarding network capacity, infrastructure availability, maintainability, and the cost-effectiveness of maintenance. This study focuses [...] Read more.
In Europe’s changing transport landscape, railways are experiencing a renaissance, driven by environmental advantages, cost efficiency, growing demand, and political support. Yet this growth also exposes major challenges, especially regarding network capacity, infrastructure availability, maintainability, and the cost-effectiveness of maintenance. This study focuses on these aspects, analyzing their interdependence and their impact on building a more resilient and efficient rail system. A prediction model, based on historical measurement data, is developed to forecast track behavior and assess an alternative maintenance strategy. This maintenance strategy uses novel approaches to define maintenance-triggering intervention values. The overarching goal of this work is to contribute to the improvement of predictive maintenance approaches. Findings show no technical or economic justification for the continual reduction of section lengths, a practice common in heavily used networks. Instead, results demonstrate that with improved planning and long-section tamping, both track quality and service life can at least be kept at the same level or even be enhanced. Longer section lengths positively influence performance by lowering running meter costs and potentially reducing operational downtime in the long run. To validate these interrelationship, future research will integrate a model that explicitly considers the costs of operational hindrances. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance: 2nd Edition)
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45 pages, 2614 KB  
Systematic Review
Machine Learning, Neural Networks, and Computer Vision in Addressing Railroad Accidents, Railroad Tracks, and Railway Safety: An Artificial Intelligence Review
by Damian Frej, Lukasz Pawlik and Jacek Lukasz Wilk-Jakubowski
Appl. Sci. 2026, 16(3), 1184; https://doi.org/10.3390/app16031184 - 23 Jan 2026
Cited by 4 | Viewed by 1566
Abstract
Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions [...] Read more.
Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions from machine learning, neural networks, and computer vision. We synthesize current research that leverages these sophisticated AI methodologies to mitigate risks associated with railroad accidents and optimize railroad tracks management. The scope of this review encompasses diverse applications, including real-time monitoring of track conditions, predictive maintenance for infrastructure components, automated defect detection, and intelligent systems for obstacle and intrusion detection. Furthermore, it delves into the use of AI in assessing human factors, improving signaling systems, and analyzing accident/incident reports for proactive risk management. By examining the integration of advanced analytical techniques into various facets of railway operations, this paper highlights how AI is transforming traditional safety paradigms, paving the way for more resilient, efficient, and secure railway networks worldwide. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance: 2nd Edition)
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