Infrastructure Health Monitoring and Automated Inspection Using Machine Learning and Computer Vision

Dear Colleagues,

Important developments in computer vision and machine learning-based sensing, including 2D imaging, 3D laser technologies, smartphones, and unmanned aerial vehicles (UAV), have created new possibilities for monitoring infrastructure. These technologies are rapidly transforming the fields of infrastructure condition assessment, nondestructive evaluation, and structural health monitoring of various infrastructures, including roadways, bridges, airports, ports, etc.  These sensing systems generate a large amount of high-resolution data, and AI-driven data analytics and machine learning techniques are being leveraged to process this data deluge and help turn raw data into actionable information and cost-saving decisions. Collectively, these technologies are emerging as the building blocks of digital transformation, cyber-physical systems, and smart city paradigms.

This Special Issue focuses on the latest developments in sensing and data analytics for infrastructure condition assessment and automated inspections. The primary objective of this Special Issue is to explore these exciting advancements, highlight the emerging frontiers of research in this area, and set the agenda for future research. Topics of interest in this session include, but are not limited to, infrastructure sensing, structural health monitoring (SHM), infrastructure condition assessment, machine learning and deep learning applications, computer vision-based assessment, feature extraction and data fusion, automated and robotic inspection using unmanned aerial and ground vehicles, vehicle-mounted roadway inspection, digital image correlation, and other advanced data-centered methods and technologies.

Dr. Mohamad Alipour
Prof. Dr. Yi-Chang James Tsai
Prof. Dr. Hoon Sohn
Guest Editors

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• automated inspection of infrastructure conditions
• infrastrucure sensing
• computer vision and visual sensing
• structural health monitoring (SHM)
• machine learning and deep learning
• digital image correlation (DIC)
• unmanned aerial vehicle (UAV)
• nondestructive evalaution (NDE)