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: 20 March 2026 | Viewed by 43
Special Issue Editors
Interests: railway engineering; condition monitoring (wayside/onboard); damage identification; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: railway infrastructures; train-bridge interaction; dynamic testing of structures; vibration sensors; structural health monitoring; modal identification; model calibration and validation; damage identification; drive-by methodologies; remote inspection; UAVs; computer vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
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
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
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
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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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