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Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures
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Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 30 October 2026 | Viewed by 7138

Special Issue Editors

Dr. Araliya Mosleh

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Porto, Praça de Gomes Teixeira, 4099-002 Porto, Portugal
Interests: railway engineering; condition monitoring (wayside/onboard); damage identification; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Diogo Ribeiro

E-Mail Website
Guest Editor
1. iBuilt, School of Engineering, Polytechnic of Porto, Porto, Portugal
2. COSNTRUCT, Faculty of Engineering, University of Porto, Porto, Portugal
Interests: railway infrastructures; condition assessment; remote inspection; digital twins; AI; structural health monitoring; digital construction; dynamic testing; drive-by strategies
Special Issues, Collections and Topics in MDPI journals
Dr. Amir Garmabaki

E-Mail Website
Guest Editor
Divison of Operation and Maintenance Engineering, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Luleå, Sweden
Interests: RAMS data analyst; climate change; transportation infrastructure maintenance modeling; remaining useful life estimation; software reliability; climate change adaptation
Special Issues, Collections and Topics in MDPI journals
Dr. Andreas Stollwitzer

E-Mail Website
Guest Editor
Research Unit Steel Structures, Institute of Structural Engineering, Faculty of Civil and Environmental Engineering, TU Wien, Vienna, Austria
Interests: railway bridges; structural dynamics; structural health monitoring (SHM); condition assessment of railway assets; track-bridge interaction; damping; digital twin; data-driven assessment of bridges
Prof. Dr. Necati Catbas

E-Mail Website
Guest Editor
Civil Engineering, University of Central Florida, Orlando, FL, USA
Interests: civil infrastructure systems; bridges; structural identification; structural monitoring; modal analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The sustainable transformation of the railway sector requires advanced technologies that enhance the safety, reliability, and efficiency of railways while reducing their environmental impact. Intelligent sensing systems, combined with digital twin platforms, enable real-time monitoring, predictive decision-making, and holistic life-cycle management of railway infrastructure and rolling stock. This Special Issue focuses on innovative research into such technologies and their applications in railway transport and infrastructures to address wheel/rail interaction and improve damage detection, structural and operational resilience, and dynamic performance under varying environmental conditions. It emphasizes climate-resilient design, Building Information Modeling (BIM) integration, monitoring-based structural assessment, and data-driven predictive maintenance strategies, as well as life-cycle cost and sustainability assessment, which are central to ensuring long-term value and minimal ecological footprint.

Guided by the Guest Editors’ expertise in sensing technologies, structural health monitoring, railway engineering, asset management, digital twins, and life-cycle evaluation, this Special Issue welcomes contributions from academia, industry, and infrastructure operators that demonstrate practical and scalable solutions for the future of rail transport.

Topics of Interest include (but are not limited to) the following:

  • Intelligent sensing systems for railway infrastructure and rolling stock;
  • Wheel/rail interface monitoring and damage identification techniques;
  • Digital twin applications for railway system design, operation, and maintenance;
  • Railway resilience under operational and climate-induced actions;
  • Railway dynamics and vibration-based monitoring methods;
  • Building Information Modeling (BIM) for railway infrastructure management;
  • AI and machine learning for predictive maintenance of rail assets;
  • Structural health monitoring of tracks, bridges, tunnels, and vehicles;
  • Climate resilience assessment and adaptation strategies for railways;
  • Life-cycle cost analysis and life-cycle sustainability assessment of railway systems;
  • Remote sensing and unmanned aerial systems for large-scale railway inspection;
  • Energy efficiency and low-carbon strategies for railway operations;
  • Cybersecurity and data integrity in intelligent railway networks.

Dr. Araliya Mosleh
Prof. Dr. Diogo Ribeiro
Dr. Amir Garmabaki
Dr. Andreas Stollwitzer
Prof. Dr. Necati Catbas
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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

  • damage identification techniques
  • digital twin and AI-driven technology for railways
  • sustainable railway infrastructure
  • structural health monitoring
  • resilient rail systems

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Published Papers (6 papers)

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Research

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30 pages, 4499 KB  
Article
Gap Measurement Method for Railway Switch Machines Based on the Fusion of Deep Vision and Geometric Features
by Wenxuan Zhi, Qingsheng Feng, Shuai Xiao, Xilong He, Haowei Liu, Yiyang Zou and Hong Li
Sensors 2026, 26(11), 3280; https://doi.org/10.3390/s26113280 - 22 May 2026
Abstract
The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability [...] Read more.
The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability and achieve sub-pixel precision. To address this issue, this paper proposes a gap measurement method based on the fusion of vision and geometric features (G-VFM). The method first utilizes a confidence-aware optimized YOLOv8 model to achieve robust localization of the gap region. Subsequently, an improved multi-channel U-Net is employed to extract soft-edge probability maps, based on which a 20-dimensional structured geometric descriptor is constructed. Finally, visual semantic features and geometric priors are fused for regression through an R34-Fusion two-stream residual network, and systematic errors are corrected using a weighted Huber loss combined with a piecewise linear calibration strategy. Test results on a constructed field dataset show that the proposed method achieves a Mean Absolute Error (MAE) of 0.0076 mm and a maximum error of 0.0193 mm. It achieves a 100% pass rate under an industrial tolerance of 0.02 mm, with an end-to-end inference time of 52.23 ms (~19.15 FPS), balancing both precision and efficiency. Further tests on illumination degradation, noise interference, and cross-batch evaluations indicate that the method maintains relatively stable performance across various complex scenarios. However, performance decreases significantly under extremely low-light conditions, suggesting that actual deployment may require integration with active lighting or multi-sensor fusion to ensure system reliability across all working conditions. Overall, this method achieves high-precision gap measurement under current experimental conditions and provides a feasible solution for vision-based switch machine status monitoring. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)
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25 pages, 5542 KB  
Article
A General Finite Beam on Tensionless Foundation Model for Rail Track Characterization and Evaluation
by Hamoud H. Alshallaqi and Brett A. Story
Sensors 2026, 26(9), 2897; https://doi.org/10.3390/s26092897 - 5 May 2026
Viewed by 595
Abstract
Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail [...] Read more.
Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail exhibits greater localized deformation that can lead to serious deleterious conditions. Track modulus represents a fundamental diagnostic measure of rail support, encompassing the vertical stiffness characteristics of the foundation and its resistance against downward rail movement. Existing track modulus characterization methodologies typically comprise deflection measurements of railway track (e.g., tie deflections) under known loads. Track modulus estimations result from analyzing deflection and load under assumptions of a traditional Winkler foundation, which can oversimplify mechanic relationships. Specifically, in the context of rail–ballast–subgrade interaction, a tensionless foundation permits gap development which can occur as track structure separates from the supporting ballast; additionally, track modulus may vary along the track length as conditions vary spatially. This paper presents a general analytical solution of ballasted track support characterization based on an iterative algorithm for the static response of a finite beam resting on a tensionless Winkler foundation. The method relates to multiple loads (e.g., concentrated axle loads and distributed self-weight), deflection along the track, and track condition through singularity functions, superposition of discrete support springs, and moment–curvature relationships. The model estimates rail deflections, lift-off points and shear and moment diagrams along the track. The technique permits: (1) validations against benchmark solutions and previously published results, (2) estimations of track modulus from known loads and measured deflections, and ultimately, (3) a framework for designing and processing sensor data streams for use in analyses and evaluations of railway track structure. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)
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27 pages, 3012 KB  
Article
Emergency Operation Scheme Generation for Urban Rail Transit Train Door Systems Using Retrieval-Augmented Large Language Models
by Lu Huang, Zhigang Liu, Chengcheng Yu, Tianliang Zhu and Bing Yan
Sensors 2026, 26(6), 2006; https://doi.org/10.3390/s26062006 - 23 Mar 2026
Viewed by 718
Abstract
Urban rail transit (URT) train-door failures are safety-critical and can cause cascading service disruptions, yet existing emergency operation schemes (EOSs) are often static, difficult to adapt to evolving fault patterns, and hard to verify against updated regulations. This study proposes a retrieval-augmented large [...] Read more.
Urban rail transit (URT) train-door failures are safety-critical and can cause cascading service disruptions, yet existing emergency operation schemes (EOSs) are often static, difficult to adapt to evolving fault patterns, and hard to verify against updated regulations. This study proposes a retrieval-augmented large language model (LLM) framework for executable and evidence-traceable EOS generation. Multi-source heterogeneous incident evidence (structured work orders, operational impact records, and unstructured maintenance/dispatch narratives) is normalized into a structured incident representation, and a hybrid retriever (dense + BM25) with cross-encoder reranking selects compact regulatory clauses and historical cases under a fixed context budget. The generator is fine-tuned with structured objectives to enforce schema compliance, role assignment, and citation grounding. Experiments on 776 passenger-door incidents from Shanghai URT (2019–2024) show that Hybrid + rerank achieves the best retrieval quality (Recall@5 = 0.78; Coverage@B = 0.71; FirstHit/B = 0.46). For generation, the full setting improves operational usability, reaching SchemaPass = 0.88, RoleAcc = 0.91, CiteCov = 0.73, and UsableAns = 0.83, compared with 0.15 UsableAns for a pure LLM baseline and 0.26 for prompting with RAG only. These results indicate that combining high-utility retrieval with structure- and citation-aware fine-tuning substantially improves the executability and verifiability of safety-critical operation schemes. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)
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15 pages, 2680 KB  
Article
Study and Optimal Design of the Integrated 37° Unidirectional SV-EMAT for Rapid Rail Flaw Detection
by Wei Yuan
Sensors 2025, 25(24), 7424; https://doi.org/10.3390/s25247424 - 6 Dec 2025
Cited by 1 | Viewed by 752
Abstract
The problem of poor coupling and wheel breakage is a critical issue in the rapid inspection of rails using contact piezoelectric ultrasonic technology for trolleys and vehicles. To overcome this shortcoming, a non-contact unidirectional Shear Vertical Wave EMAT (USV-EMAT) for rapid rail flaw [...] Read more.
The problem of poor coupling and wheel breakage is a critical issue in the rapid inspection of rails using contact piezoelectric ultrasonic technology for trolleys and vehicles. To overcome this shortcoming, a non-contact unidirectional Shear Vertical Wave EMAT (USV-EMAT) for rapid rail flaw detection with a larger emission angle is proposed and optimized. First, the core characteristics of the USV-EMAT and the Unidirectional Line-Focusing Shear Vertical Wave EMAT (ULSV-EMAT) are compared and analyzed, including emission angle, directivity, intensity, and detection scan distance. The results confirmed that the USV-EMAT is more suitable for rapid rail flaw detection. Secondly, the orthogonal experimental analysis method was used to optimize the structural parameters of the probe. This study systematically identified the key factors influencing the directivity and intensity of acoustic waves excited by the probe, as well as the detection blind zones. Finally, the structural parameters of the integrated 37° USV-EMAT probe were determined by comparing and analyzing the received signal characteristics of the transmit–receive racetrack coil and the self-transmitting–receiving meander coil. The results show that the optimized probe achieves a 14.3 dB SNR for detecting a 5 mm diameter, 50 mm deep transverse hole in the rail, and a 14.0 dB SNR for a 3 mm diameter, 25 mm long, 50 mm deep flat-bottomed hole. Additionally, this study reveals that as the burial depth of the transverse holes increases, the detection scan distance for such defects exhibits an “N”-shaped trend, with the minimum occurring at a depth of 90 mm. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)
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Review

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50 pages, 13482 KB  
Review
Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System
by Mahyar Jafar Kazemi, Maria Rashidi, Won-Hee Kang and Mohammad Siahkouhi
Sensors 2026, 26(8), 2333; https://doi.org/10.3390/s26082333 - 9 Apr 2026
Viewed by 1405
Abstract
Digital Twin (DT) technology is increasingly recognised as a promising approach for predictive and optimised railway maintenance; however, its current applications remain fragmented and lack systematic evaluation across railway domains. This study aims to critically review DT-enabled monitoring, analysis, and maintenance decision-support systems [...] Read more.
Digital Twin (DT) technology is increasingly recognised as a promising approach for predictive and optimised railway maintenance; however, its current applications remain fragmented and lack systematic evaluation across railway domains. This study aims to critically review DT-enabled monitoring, analysis, and maintenance decision-support systems in railway engineering, while identifying key research gaps and future directions. A DT is defined in this study as an integrated cyber–physical system comprising a physical asset, its virtual representation, and continuous bidirectional data exchange enabling real-time monitoring, prediction, and decision-making. A systematic and transparent review methodology was adopted to select 34 representative peer-reviewed studies published between 2020 and 2025, focusing explicitly on DT applications in railway infrastructure and operations. Among these, a subset of 10 key studies was further analysed in greater depth based on their level of technical implementation, data integration capability, and relevance to predictive maintenance applications, which cover multiple domains, including track systems, rolling stock, bridges, and communication networks. Results show that DT-based approaches can enhance fault detection, enable condition-based and predictive maintenance, and reduce reliance on manual inspections. However, significant limitations remain. Most studies are conceptual or pilot-scale, with limited validation under real operating conditions. Key challenges include a lack of standardisation and interoperability, constraints in real-time scalability, data governance and cybersecurity issues, and insufficient integration of multi-source sensing and advanced analytics. This review provides a structured synthesis of current DT implementations in railway systems and highlights critical gaps that must be addressed to enable scalable, reliable, and fully integrated DT-driven maintenance frameworks. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)
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32 pages, 3278 KB  
Review
Advancing Circular Economy Implementation for High-Speed Train Rolling Stocks by the Integration of Digital Twins and Artificial Intelligence
by Lalitphat Khongsomchit and Sakdirat Kaewunruen
Sensors 2025, 25(20), 6473; https://doi.org/10.3390/s25206473 - 20 Oct 2025
Cited by 2 | Viewed by 2837
Abstract
This paper presents a state-of-the-art review on the integration of digital twins and artificial intelligence to advance the circular economy and the 10R principles implementation in high-speed train rolling stock. Rolling stock generates substantial waste at the end of its service life, yet [...] Read more.
This paper presents a state-of-the-art review on the integration of digital twins and artificial intelligence to advance the circular economy and the 10R principles implementation in high-speed train rolling stock. Rolling stock generates substantial waste at the end of its service life, yet the application of the circular economy and the 10R principles (Refuse, Rethink, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, and Recover) in this domain remains limited compared with infrastructure. The review analyses 47 studies retrieved from Web of Science and IEEE Xplore, focusing on digital twin applications in railway infrastructure and rolling stock, and machine learning techniques. Findings reveal that most studies concentrate on data management and efficiency improvement, while only a few explicitly address the circular economy and 10R principles. A comparative analysis of high-waste components against current machine learning applications further highlights critical gaps. To address these, an automated workflow is proposed, incorporating digital twins, artificial intelligence, and the 10R principles to support condition monitoring and sustainable resource management. The study provides insights and research directions to enhance sustainability in railway asset management. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)
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