Title: AI-enriched SHM of civil engineering structures

Organisers: Enrique García-Macías (University of Granada), Ayan Sadhu (Western University), Ilaria Venanzi (University of Perugia)

Artificial Intelligence (AI) has revolutionized the field of Structural Health Monitoring (SHM) in various ways. From data analytics, anomaly detection, and predictive maintenance to pattern recognition and adaptive monitoring, AI-driven SHM systems provide engineers with valuable insights and tools for maintaining the safety and longevity of critical and aging infrastructure. With the escalating global emphasis on the value of information and digitalization, deep learning AI stands poised to deliver a substantial leap beyond conventional machine learning approaches, offering unprecedented potential for robust damage identification of civil structures using a wide range of SHM data sources, including time series, images, and videos.

This special session invites contributions focusing on innovative uses of AI techniques for SHM for structures and infrastructures. Among others, the topics to be addressed include:

• Deep Learning for SHM data analytics
• Image processing and augmented reality for structural diagnosis
• AI-based Remote Monitoring and Automated Diagnostics
• Integration with IoT and robotic sensing technologies in SHM
• Transfer Learning applications
• AI-driven population-Based SHM
• Generative adversarial networks for synthetic data generation
• Digital twins and visualization of SHM data