MS 30

Bayesian Inference for Model Updating, Damage Identification, and Reliability Updating of Dynamical Systems

Organizers

Wang-Ji Yan,
State Key Lab of Internet of Things for Smart City, University of Macau, China

Costas Papadimitriou,
Department of Mechanical Engineering, University of Thessaly, Greece

Michael Beer,
Institute for Risk and Reliability, Leibniz University Hannover, Germany

Matteo Broggi,
Institute for Risk and Reliability, Leibniz University Hannover, Germany

Sifeng Bi,
University of Southampton, UK

Yanlin Zhao,
Department of Electromechanical Engineering, University of Science and Technology Beijing, China

Abstract

Significant advancements in sensing, communication, and computer technologies have facilitated the integration of structural health monitoring data into the processes of structural model updating, damage identification, and reliability updating, making them increasingly prominent research topics in civil, aerospace, and mechanical engineering. During the stages of data collection, modeling, and analysis, uncertainties arising from noise contamination, modeling errors, and operational, environmental, and manufacturing variabilities are inevitable. The combination of these uncertainties distorts essential information that reflects the true state of structures, leading to discrepancies in practical applications. Therefore, it is crucial to investigate these uncertainties to enhance the robustness and accuracy of model updating, damage identification, and reliability updating techniques, ultimately ensuring the safety of existing structures. The objective of this Mini-Symposium is to provide a platform for scientists and engineers from academia and industry to present their state-of-the-art research on Bayesian inference for model updating, damage identification, and reliability updating. By doing so, we aim to advance current practices in structural safety and resilience. The topics of interest include, but are not limited to:

• Recent advances in stochastic sampling approaches for Bayesian updating
• Novel variational inference schemes for Bayesian updating
• Recent advances in Bayesian system identification technologies
• Hierarchical Bayesian updating considering environmental, operational and manufacturing variabilities
• Bayesian inference for reliability updating of structural dynamical systems
• Sequential Bayesian updating for structural physical models and reliability
• Online Bayesian inference schemes integrating streaming structural health monitoring data
• Bayesian updating utilizing advanced surrogate model
• Prediction of dynamical responses within a Bayesian framework
• Bayesian machine learning for structural model updating and damage identification
• Bayesian deep learning for structural reliability updating
• Engineering practice of Bayesian model updating and reliability updating
• Bayesian filtering techniques for input-state-parameter estimation of linear/nonlinear models