MS09 Structural Health Monitoring and Model Identification in Uncertain Environments
Dr. Matteo Broggi: firstname.lastname@example.org
Matteo Broggi, Dr., Institute for Risk and Reliability, Leibniz University of Hannover, email@example.com
Chul-Woo Kim, Prof., Department of Civil & Earth Resources, Kyoto University, firstname.lastname@example.org
Yi Zhang, Prof., Department of Civil Engineering, Tsinghua University, email@example.com
Sifeng Bi, Dr., School of Aerospace Engineering, Beijing Institute of Technology, firstname.lastname@example.org
Michael Beer, Prof., Institute for Risk and Reliability, Leibniz University of Hannover, email@example.com
David Moens, Prof., Department of Mechanical Engineering, KU Leuven, firstname.lastname@example.org
Abstract of the special session：
Structural Health Monitoring assures the functionality of critical structures by detecting and characterizing the damage accumulated in their life times by monitoring the changes in time of the material properties, configuration or load carrying capabilities.
In addition to the uncertainties traditionally affecting the structural parameters, SHM needs to deal with the inherently uncertainty shown by degradation and aging processes. Because of this, traditional probabilistic methods might not suffice to cover the full spectrum of uncertainties that might appear in ageing structures. A robust structural model must be taken into account to accurately identify and characterize damage level and remaining life, as well as update the actual reliability level of an aged structure. Popular emerging techniques are now available in the field of computational mechanics, which can be employed to assist in the monitoring of the health of the structures.
The scope of this mini-symposium is to bring together experts researchers, academics and practicing engineers concerned with the various aspects of Structural Health Monitoring. Contributions addressing developments in the theoretical, and computational approaches as well as practical applications are invited, such as Stochastic model updating, optimal sensor placement, system identification, damage detection and Bayesian approaches, interval models, etc.
ICOSSAR 2021-2022 Secretariat
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