MS Title:
Machine Learning for Structural Control and Health Monitoring
 

Description:
In recent years, machine learning (ML) techniques have emerged as powerful tools for advancing the fields of structural control and health monitoring. ML algorithms offer new opportunities to enhance the efficiency, accuracy, and reliability of structural control systems and health monitoring practices. These techniques can be applied to a wide range of problems, including real-time structural response prediction, damage detection, anomaly detection, and optimization of control strategies. This mini-symposium aims to provide a comprehensive platform for researchers, practitioners, and experts to delve into the latest developments, challenges, and applications of ML in structural control and health monitoring. We anticipate that this mini-symposium will contribute to advancing the understanding and application of machine learning in structural control and health monitoring. The topics to be covered include, but are not limited to:

• ML-based real-time structural health monitoring
• Predictive modeling of structural behavior using ML
• Damage detection and localization using ML algorithms
• Anomaly detection for structural safety and performance assessment
• ML-enhanced control strategies for structural systems
• Optimization of structural control systems with ML techniques
• Data-driven approaches to improve structural resilience
   

Session Chairs:
Pei Pei, Jiangsu University of Science and Technology, Jiangsu, China. E-mail: peipei@just.edu.cn
Ser Tong Quek, National University of Singapore, Singapore, Singapore. E-mail: st_quek@nus.edu.sg
Yongbo Peng, Tongji University, Shanghai, China. E-mail: pengyongbo@tongji.edu.cn