Guest Editors
Assoc. Prof. Yunpeng Wu
Email: wuyunpeng@kust.edu.cn
Affiliation: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Homepage:
Research Interests: Intelligent Inspection of Transportation Infrastructure, Structural health monitoring, Computer vision, Edge computing
Prof. Fengxiang Guo
Email: guofengxiang@kust.edu.cn
Affiliation: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Homepage:
Research Interests: Traffic safety and simulation, Driving behavior, Traffic psychology
Dr. Yixuan Geng
Email: yxgengbjtu@gmail.com
Affiliation: Department of Mechanical Engineering, University of Hong Kong, Hong Kong 999077, China
Homepage:
Research Interests: AI-enhanced UAV systems for structural defect detection in transportation infrastructure; 3D point cloud processing and semantic analysis using deep learning; Reinforcement learning for collaborative UAV swarm intelligence
Dr. Zhiwei Cao
Email: zhiwei@bjtu.edu.cn
Affiliation: State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University,Beijing 100044, China
Homepage:
Research Interests: Intelligent transportation, Railway safety, Infrastructure disease detection; Machine vision
Assoc. Prof. Zhipeng Zhang
Email: zp.zhang@sjtu.edu.cn
Affiliation: Department of Transportation Engineering, Shanghai Jiao Tong University,Shanghai 200030, China
Homepage:
Research Interests: Intelligent transportation, transportation infrastructure maintenance, low-altitude economy
Summary
Structural health monitoring (SHM) is a critical research theme that ensures the safety, longevity, and efficiency of critical infrastructures in civil engineering field. This special issue focuses on integrating artificial intelligence (AI) and low-altitude technologies to advance health monitoring and safety management of transportation systems-including but not limited to road and railroad infrastructure (e.g., bridges, tunnels, and ancillary structures)-amidst the emerging challenges of the low-altitude economy.
With the rapid expansion of drone-based logistics, urban air mobility, and aerial inspections under the low-altitude economy, traditional infrastructure faces dynamic loads, vibration impacts, and heightened demands for real-time, high-frequency monitoring. This Special Issue seeks research leveraging AI-driven computer vision (CV) and multi-source sensing data (e.g., visible/infrared images, videos, LiDAR point clouds) to enable automated damage detection, 3D defect reconstruction, safety evaluation and predictive maintenance Key technologies include drone-mounted sensors, ground-penetrating radar (GPR), wireless networks, and robotic systems, combined with advanced analytics (e.g., real-time haze/rain removal, model pruning for edge computing).
We invite contributions addressing low-altitude economy-specific challenges, such as:
· Transportation infrastructure SHM or safety evaluation under dynamic environments (e.g., moving drones, weather interference)
· AI-enhanced anomaly detection for early warning of structural degradation
· Sensor fusion frameworks integrating drone-captured data with IoT networks
· Sustainable SHM systems balancing accuracy, computational efficiency, and environmental impacts
This Special Issue aims to bridge the gap between intelligent SHM methodologies and the operational realities of the low-altitude economy, offering scalable solutions for researchers and practitioners to ensure infrastructure resilience in an era of evolving transportation paradigms.
Keywords
Structural health monitoring (SHM); Low-altitude economy; safety evaluation; Artificial intelligence (AI); Drone-based inspection; Computer vision;
Published Papers
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