MS07 Uncertainty Modelling, Quantification and Propagation in System Identification and Structural Damage Detection
Associate Prof. Wangji Yan: email@example.com
Wangji Yan, Associate Professor, University of Macau, firstname.lastname@example.org
Kaveng Yuen, Professor, University of Macau, email@example.com
Costas Papadimitriou, Professor, University of Thessaly, firstname.lastname@example.org
Michael Beer, Professor, Leibniz Universität Hannover, email@example.com
Lambros Katafygiotis, Professor, Hong Kong University of Science and Technology, firstname.lastname@example.org
Abstract of the special session：
System identification and structural damage detection have increasingly become a hot research topic in the field of civil, aerospace and mechanical engineering. Although great achievements have been made in sensing, communication and computer technologies, a lack of accurate and reliable techniques to interpret measured data still challenges the whole community. Uncertainties due to noise contamination, modelling error and environmental variability inevitably arises in the process of data collection, data modelling and analysis. The combination of these uncertainties will distort intrinsic information reflecting the real state of structures, thereby leading to aberrations in real applications.
Therefore, investigating the uncertainties is crucial for improving the robustness and accuracy of system identification and structural damage detection techniques, which can provide a more solid foundation for assessing the operational condition of existing structures. The aim of this Mini-Symposium is to provide a forum in which scientists and engineers from academia and industry can present their state-of-the-art research results on uncertainty modelling, quantification and propagation technology to augment current system identification and structural damage detection practice. The topics of interest include, but are not limited to: probabilistic modelling for dynamic responses, theoretical and experimental system identification with uncertainty, stochastic simulation techniques for state estimation, filtering techniques for input-state-parameter estimation of linear and nonlinear models, Bayesian approach for model class selection, structural diagnosis and prognosis techniques accommodating uncertainties, updating reliability using measurements, optimal experimental design techniques, value of information in structural health monitoring, confidence-based decision-making, etc.
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