Artificial-Intelligence-Based Methods for Structural Health Monitoring

Dear Colleagues,

The area of intelligent and resilient infrastructure and smart cities is a rapidly emerging field that is redefining the future of urban development, and how to preserve the existing infrastructure against natural hazards. Sensing, and especially networked sensing and monitoring, has been an integral part of a growing field. Analysis and interpretation of a large volume of data collected by the sensor network, or digital images, and extraction of critical information that can determine the state of health, reliability, and safety and life cycle assessment of these infrastructures, including feature extraction, require the development of advanced and more realistic computational models and analysis tools that can predict the behavior of these systems under complex and even hazardous loading environments and identify potential sources of damage and deterioration in real time.     

Over the past several years, a series of artificial-intelligence-based methodologies, including machine learning methods, have been proposed for model updating, diagnostics, data interpretation, and feature extraction for the heath monitoring of infrastructure systems. This rapidly emerging field of research has demonstrated superiority for system identification, feature extraction, damage identification, and even direct response prediction of dynamical systems and has shown promises for a wide range of practical applications.

This Special Issue aims to underscore the importance of development and introduction of AI-based methodologies for structural health monitoring of infrastructure systems and the analysis and feature extraction from sensor data. Potential topics include but are not limited to the following areas and utilization of AI-based methods for structural health monitoring:

• Artificial neural networks;
• Deep learning neural networks;
• System identification;
• Surrogate models;
• Big data in infrastructure systems
• Optimization;
• Probabilistic methods for SHM combined with AI methods;
• Various machine learning tools;
• Dynamic response prediction via AI methodologies;
• Feature extraction schemes.

Prof. Mohammad Noori
Guest Editor

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