Dr. Zhishen Wu is now president of Henan University of Technology, a chair professor at Southeast University in China and a professor emeritus at Ibaraki University in Japan. His research interests include carbon/basalt fibers and FRP composite technologies, advanced sensor technologies and structural health/risk/disaster monitoring, control and management.
He has published 16 books and over 600 papers in refereed journals, holds over 100 authorized patents and contributed to 30 standards and guidelines as one of the leaders. His many awards include the JSCE Research Award in 1990, the JSCM Technology Award in 2007, 2009 International SHM Person of the Year Award, IIFC medal in 2016 and the Aftab Mufti Life-Time Achievement Medal of ISHMI in 2023, National Prize for Progress in Science &Technology and for Technology Innovation of China in 2012 and 2017 etc.
He is currently the president of the International Society for Basalt Fibers and Composites (ISBFC) and has served as the president of ISHMII for 2017-2023. He serves as an editor, associate editor, editorial board member for more than ten international journals, including as the founding Editor-in-Chief of the International of Sustainable Materials and Structural Systems. He is a fellow of ASCE, JSCE, ISHMII, IIFC and an international fellow of the Engineering Academy of Japan, the Canadian Academy of Engineering and the European Academy of Sciences and Arts, etc.

Abstract:
Local damage identification based on changes in structural dynamic characteristics is an important issue in structural health monitoring and safety assessment of civil engineering structures. Global dynamic characteristics obtained from vibration responses have provided a well-established theoretical and methodological basis for identifying global stiffness degradation and significant structural damage. However, because civil engineering structures are generally large in scale and local damage often
induces only limited changes in global dynamic characteristics, conventional vibration- based methods remain limited in their sensitivity and serviceability to early-stage damages.
To address these challenges, this keynote first presents multi-scale dynamic damage identification methods based on acceleration, deformation, and macro-strain responses.
To enhance the sensitivity of acceleration and dynamic deformation responses to local damages, wavelet neural networks and AI-based methods are employed to extract damage-sensitive features from structural dynamic responses. To improve the identification of early-stage damages, dynamic macro-strain distribution is employed to develop strain-mode-based damage identification algorithms for various damage scenarios, including cracking, corrosion, wire breakage, scour, and localized stiffness degradation. Together, these approaches provide a multi-scale framework for damage identification from global structural responses to local damage.
Building upon damage identification, AI-driven models are further developed for structural performance assessment. Damage-sensitive features extracted from acceleration, dynamic deformation, and macro-strain responses provide a generalized data-driven basis for characterizing structural performance and its degradation. By integrating monitoring responses, identified damage features, and performance information, the relationship between structural dynamic characteristics and performance states can be established for condition assessment.
Furthermore, for structural-level safety and reliability assessment, the knowledge reasoning and multi-source information fusion capabilities of AI large models are utilized to integrate monitoring data, damage identification results, and structural performance information. Bayesian probabilistic analysis is incorporated to account for uncertainties and update structural state information, establishing an intelligent assessment framework linking vibration-based monitoring, local damage identification, performance assessment, and structural reliability. These key technologies have been applied to the Dongwuyang Grand Bridge, the Huangmaohai Cross-Sea Passage, the Nanjing Yangtze River Bridge, and various other large-scale civil infrastructure projects.