MS Title:
Dealing with Uncertainties in System Identification and Structural Health Monitoring

Description:
System identification and structural health monitoring have increasingly become hot research topics in the fields of civil, aerospace, and mechanical engineering. Despite significant advancements in sensing, communication, and computer technologies, the lack of accurate and reliable techniques to interpret measured data continues to challenge the entire community. Uncertainties arising from noise contamination, modeling errors, operational and environmental variabilities inevitably emerge during the process of data collection, modeling, and analysis. The combination of these uncertainties distorts intrinsic information that reflects the true state of structures, leading to aberrations in practical applications. Therefore, investigating these uncertainties is crucial for improving the robustness and accuracy of system identification and structural health monitoring techniques, ultimately providing a more solid foundation for assessing the operational condition of existing structures. The aim of this Mini-Symposium is to offer a platform for scientists and engineers from academia and industry to present their state-of-the-art research results on uncertainty quantification and propagation technology, thereby enhancing current practices in system identification and structural health monitoring. The topics of interest include, but are not limited to:

• Recent advances in Bayesian system identification technologies 
• Hierarchical Bayesian updating considering environmental and operational variability 
• Probabilistic deep learning for structural damage detection 
• Uncertainty quantification for machine learning in structural identification 
• Reliability updating for engineering structures based on dynamical measurements
• Non-probabilistic methods for structural system identification and damage detection  
• Engineering practice of structural health monitoring accommodating uncertainties 
• Theoretical and experimental system identification with multiple uncertainties 
• Structural diagnosis and prognosis techniques accommodating uncertainties
• Filtering techniques for input-state-parameter estimation of linear/nonlinear models
• Optimal experimental design techniques
• Value of information in structural health monitoring, 
• Confidence-based decision-making 
   

Session Chairs:
Wang-Ji Yan, State Key Lab of Internet of Things for Smart City and Department of Civil Engineering, University of Macau. E-mail: wangjiyan@um.edu.mo
Xinyu Jia, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Mechanical Engineering, Hebei University of Technology.  E-mail: xinyujia@hebut.edu.cn
Costas Papadimitriou, Department of Mechanical Engineering, University of Thessaly.  E-mail: costasp@uth.gr