Structural Health Monitoring Using Computer Vision-Aided Sensing Technologies
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 138
Special Issue Editors
Interests: structural health monitoring; computer vision; digital twin; robot-based mobile inspection
Interests: reinforced concrete; vibration-based structural system; machine learning; AI
Special Issues, Collections and Topics in MDPI journals
Interests: smart infrastructure; AI-assisted design; construction automation
Interests: structural health monitoring; nondestructive testing; acoustic emission
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, with the advancement of artificial intelligence (AI), computer vision has gained great traction and achieved leapfrog development in the field of structural health monitoring. Computer vision methods, when integrated with emerging technologies such as AI, remote cameras, multi-sensor fusion, the Internet of Things (IoT), Building Information Modeling (BIM), wireless communications, digital twin management, unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), etc., have a great potential to provide automatic, robust, accurate, and efficient solutions to the condition assessment of civil infrastructure, contributing to a smarter and more resilient built environment.
This Special Issue invites contributions on the latest development and application of computer vision technologies in the area of structural health monitoring. Topics of interest include, but are not limited to, the following:
- State-of-the-art review on computer vision-based structural health monitoring.
- Computer vision methods for structural and nonstructural damage detection, localization, and quantification at the component level, system level, and regional level.
- Generative AI for synthetic dataset generation to enhance the accuracy and robustness of computer vision models for structural health monitoring.
- Integration of computer vision with large language models (LLMs) for multi-objective structural health monitoring.
- Computer vision methods for measurement of structural displacements, velocities, and accelerations.
- Novel hardware design of vision-based sensory systems and integration of vision systems with other sensory systems for multi-objective structural health monitoring.
- Integration of computer vision with IoT, BIM, and mobile robots for structural health monitoring.
Dr. Xiao Pan
Dr. Yasutaka Narazaki
Dr. Qipei Mei
Dr. Li Ai
Dr. Jianze Wang
Guest Editors
Manuscript Submission Information
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Keywords
- structural health monitoring
- condition assessment
- computer vision
- sensors and robotics
- digital twin
- internet of things
- artificial intelligence
- machine learning
- deep learning
- reinforcement learning
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