On June 17 (9:00am CST), Prof. Yuguang Fu hosted a webinar about “advanced artificial intelligence for structural health monitoring”, which attracted 54 participants for this two-hour discussion. We were honored to have Prof. from Genda Chen from Missouri University of Science and Technology, USA and Prof. Jun Li from Curtin University, Australia as our distinguished speakers.
Prof. Gen delivered a talk, titled “AI Generalizability, Approximability, Traceability, and Explainability in Civil Infrastructure”. He introduced the GATE framework for trustworthy AI in civil engineering, presenting AI copilot strategies that integrate domain expertise throughout model development to enhance transparency, reliability, and practical adoption. Applications in bridge inspection and strength prediction were also showcased. In particular, he promoted the novel concept of progressive learning techniques to enable both explainability and approximability well.
Prof. Li made a presentation, titled “Computer Vision and AI-enabled Infrastructure Intelligence”. He showed recent advances in computer vision and AI for smart infrastructure, covering vision-based structural sensing, vibration displacement measurement, modal identification, and structural health monitoring. The talk also showcased AI applications in data generation and crack segmentation, validated through laboratory experiments and real-world field demonstrations.
The inspiring talks sparked a vibrant panel discussion on how advanced AI is reshaping the future of SHM. Participants exchanged perspectives on several emerging topics, including population-based SHM, edge intelligence for real-time infrastructure monitoring, and the generalizability, traceability, and explainability of AI models for engineering applications. The discussion also highlighted the importance of integrating domain knowledge and knowledge transfer with AI to develop trustworthy, scalable, and practical solutions for challenges faced by real-world systems.
Link
https://1drv.ms/v/c/67647b83ed1aae5f/IQACS3-U7zkwTY5gzltAzvVQASfTQ4OaMhG-27XSmEXqzv0?e=ggWa3Z
