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 October 2025 | Viewed by 26
Special Issue Editors
Interests: reliability engineering; maintenance; maintainability analysis; virtual maintenance; maintenance strategy
Special Issues, Collections and Topics in MDPI journals
2. Department of Civil and Environmental Engineering, University of Macau, Taipa, Macau
Interests: AI-powered structural dynamics; seismic-resistant design; structural health monitoring
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
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Keywords
- railway infrastructure
- health monitoring
- artificial intelligence
- digital twins
- big data analytics
- IOT-based systems
- risk assessment
- sustainable railway operations
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