Innovations in Damage Detection Methods for the Built Environment

Structural damage - whether caused by corrosion, fatigue, or excessive deformation - poses significant threats to the safety and integrity of structures. Structural health monitoring and non-destructive testing (NDT) approaches are developed to detect damage, but they often falter when applied to real-world structures. However, recent advancements in robotics and innovative computing techniques such as artificial intelligence, the Internet of Things (IoT), computer vision, data mining, and deep learning present promising solutions that overcome the limitations of conventional damage detection methods.

This Special Issue aims to provide a platform for sharing new theories, techniques, and methodologies related to the damage detection of structures through the application of emerging intelligent tools and advanced computing techniques. By exploring these novel approaches, we hope to enhance the sustainability, automation, cost-effectiveness, and smartification of structural damage detection.
We invite original research articles, high-quality reviews, as well as engineering practice applications and case studies that demonstrate the utility of these smart damage detection methods.

Topics for submissions include but not limited to:

• Vibration-based damage detection
• Non-contact damage detection
• Computer vision-based damage detection
• Artificial intelligence-based damage detection
• Robotic tools for damage detection
• Structural control and health monitoring
• System identification and modal analysis
• Damaging process and crack propagation
• Non-destructive testing for damage detection
• Experimental, numerical, and analytical studies on damage detection

By shedding light on these emerging intelligent tools and computing techniques, we can pave the way for more robust and reliable structural integrity in the future.