Special lssues

Advanced Computer Vision Methods and Related Technologies in Structural Health Monitoring

Submission Deadline: 31 December 2024 Submit to Special Issue

Guest Editors

Dr. Yunpeng Wu
Kunming university of science and technology, China.
Email: wuyunpeng@kust.edu.cn

Dr. Zhenkun Li
Aalto University, Finland.
Email: zhenkun.li@aalto.fi

Dr. Chuang He
Taizhou University, China.
Email: hechuang@szu.edu.cn

Dr. Li Ai
University of South Carolina, USA.
Email: ail@email.sc.edu


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 the key area of civil infrastructure health monitoring and inspection, with a specific emphasis on artificial intelligence (AI) technologies, especially computer vision (CV), combined with the sustainable monitoring data, such as visible-light image, infrared image, video, cloud point, etc.


This special issue aims to implement advanced intelligence technologies to monitor the structural health status of various devices and infrastructures in civil engineering, including railroad tracks, catenary, bridges, noise barrier, tunnels, etc. This will involve the utilization of cutting-edge sensors, such as high-definition camera, Ground Penetrating Radar (GPR), wireless sensor network, or drone sensing system.


This special issue is dedicated to promoting the development of reliable and environmentally friendly structural health monitoring practices of civil infrastructure by integrating new intelligence methods and related technologies. This special issue welcomes original research articles and review articles. The topics include but are not limited to the following:

• CV-based infrastructure inspection, damage measurement and assessment, etc.

• Application of AI, drone, robot, GPR, and new technologies in SHM

• Real-time and automated SHM systems

• Image/video haze/rain removal for SHM

• Dynamic and static behavior analysis of infrastructure based on monitoring information

• 3D reconstruction and modeling of structural defects

• Model pruning and deployment methods for real-time monitoring


Computer Vision, Artificial Intelligence, Structural Health Monitoring, Camera, Lidar, GPR

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