Modeling, Sensing, Diagnosis and Lifecycle Maintenance for Rail Transit Infrastructure

Dear Colleagues,

Rail transit infrastructure, as the backbone of efficient and sustainable urban and intercity transportation, plays a crucial role in supporting economic development and improving people's travel experience. With the rapid expansion of rail networks worldwide and the increasing demands for operational safety, reliability, and sustainability, the industry is facing unprecedented challenges in ensuring the long-term performance of infrastructure such as tracks, bridges, and tunnels. Conventional approaches to dynamics analysis, defect detection, and maintenance management are gradually insufficient to address the complex and dynamic issues arising from aging assets, heavy traffic loads, and environmental uncertainties.

This Special Issue aims to showcase cutting-edge research and practical advancements in the interdisciplinary field of rail transit infrastructure, focusing on the integration of modeling, sensing, diagnosis, and lifecycle maintenance technologies. It seeks to bridge the gap between theoretical dynamics analysis and engineering applications, promote the innovation of intelligent sensing and diagnostic methods, and explore optimized lifecycle maintenance strategies that enhance infrastructure resilience and reduce lifecycle costs. By bringing together scholars, researchers, and practitioners from diverse backgrounds including rail engineering, dynamics modeling, intelligent sensing, signal processing, and maintenance management, this Special Issue will provide a platform for exchanging novel ideas, methodologies, and case studies.

We welcome high-quality original research articles and systematic literature reviews that address the following (but not limited to) topics:

• Modeling for rail transit dynamics: Vehicle-track/bridge/subgrade/tunnel coupled dynamics modeling, multi-physics coupling analysis (e.g., mechanics-electronics-thermal), uncertainty quantification in dynamics models, and dynamics-based performance evaluation of infrastructure.
• Intelligent sensing technologies: Advanced sensing systems, distributed sensing for large-scale infrastructure, wireless sensor networks, and data acquisition and transmission technologies under harsh rail environments.
• Defect diagnosis and condition assessment: Signal processing and feature extraction for defect detection, machine learning and deep learning-based diagnostic algorithms, damage identification and localization, and condition grading methods for rail transit assets.
• Lifecycle maintenance and management: Predictive maintenance based on condition monitoring data, reliability-centered maintenance strategies, lifecycle cost analysis and optimization, digital twins for maintenance decision-making, and resilient maintenance planning under extreme events.

We sincerely appreciate your interest in this Special Issue and look forward to receiving your valuable contributions that will advance the state-of-the-art in rail transit infrastructure engineering. We also encourage you to share this call with your colleagues and peers who may be interested in participating, to collectively promote the development of safer, more reliable, and sustainable rail transit systems.

Dr. Lifeng Xin
Prof. Dr. Lizhong Song
Guest Editors

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