Research Topic

Next-Generation Structural Health Monitoring Strategies using Artificial Intelligence

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About this Research Topic

In the modern age of globalization and climate change, large-scale infrastructure has undergone accelerated aging and reduction in life span. Exponentially increasing population and traffic, unpredictable natural hazards, and human-made events have resulted in frequent disruptions in the operational state of the structure. Deficient structures can potentially jeopardize public safety, produce important economic losses, and even lead to catastrophic failures if critical damages are not detected in a timely manner. In the last few decades, structural health monitoring (SHM) systems have emerged as powerful solutions to detect, localize, and quantify critical damages in structures. However, there is still an important gap between the discovery of such damage and condition-based maintenance decisions, attributable to the complexities and sizes of structural systems under consideration. A solution is the incorporation of machine learning strategies, also known as artificial intelligence (AI), that can be used to infer causalities and yield decision strategies based on streaming data and physical information. In particular, the advent of next-generation sensors (such as high-speed cameras, drones, smart phones and unmanned ground vehicles) is enabling the acquisition of large data sets from dramatically improved temporal and spatial resolutions, yielding big data opportunities. AI algorithms can now be harnessed to analyze such big data of critical infrastructure in real-time and automated fashion.

Considering the growing trend of AI-based methods, this Research Topic aims to attract research contributions in AI-assisted next-generation SHM strategies from the scientific community worldwide. It is anticipated that the upcoming publications in this emerging topic will be of significant interest to both academics and practitioners in the field of structural monitoring and maintenance. The objective of this Research Topic is to generate discussions on the latest advances in interdisciplinary research on leveraging artificial intelligence for SHM applications, with a focus on decision-enabling systems.

Topics of interest include, but are not limited to:
• Algorithms harnessing big data for SHM applications
• Machine learning in structural monitoring and control
• Cyber-security for civil infrastructure
• Cyber-physical systems for critical structures
• Big data management and visualization
• Injection of physical knowledge in data-driven strategies
• Integrated smart sensing systems and data fusions
• Real-time monitoring and automated structural inspection


Keywords: Artificial Intelligence, Structral Health Monitoring, Smart Sensors, Big Data, Data Fusion


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

In the modern age of globalization and climate change, large-scale infrastructure has undergone accelerated aging and reduction in life span. Exponentially increasing population and traffic, unpredictable natural hazards, and human-made events have resulted in frequent disruptions in the operational state of the structure. Deficient structures can potentially jeopardize public safety, produce important economic losses, and even lead to catastrophic failures if critical damages are not detected in a timely manner. In the last few decades, structural health monitoring (SHM) systems have emerged as powerful solutions to detect, localize, and quantify critical damages in structures. However, there is still an important gap between the discovery of such damage and condition-based maintenance decisions, attributable to the complexities and sizes of structural systems under consideration. A solution is the incorporation of machine learning strategies, also known as artificial intelligence (AI), that can be used to infer causalities and yield decision strategies based on streaming data and physical information. In particular, the advent of next-generation sensors (such as high-speed cameras, drones, smart phones and unmanned ground vehicles) is enabling the acquisition of large data sets from dramatically improved temporal and spatial resolutions, yielding big data opportunities. AI algorithms can now be harnessed to analyze such big data of critical infrastructure in real-time and automated fashion.

Considering the growing trend of AI-based methods, this Research Topic aims to attract research contributions in AI-assisted next-generation SHM strategies from the scientific community worldwide. It is anticipated that the upcoming publications in this emerging topic will be of significant interest to both academics and practitioners in the field of structural monitoring and maintenance. The objective of this Research Topic is to generate discussions on the latest advances in interdisciplinary research on leveraging artificial intelligence for SHM applications, with a focus on decision-enabling systems.

Topics of interest include, but are not limited to:
• Algorithms harnessing big data for SHM applications
• Machine learning in structural monitoring and control
• Cyber-security for civil infrastructure
• Cyber-physical systems for critical structures
• Big data management and visualization
• Injection of physical knowledge in data-driven strategies
• Integrated smart sensing systems and data fusions
• Real-time monitoring and automated structural inspection


Keywords: Artificial Intelligence, Structral Health Monitoring, Smart Sensors, Big Data, Data Fusion


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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