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PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Edited by: Y. Tsompanakis, J. Kruis and B.H.V. Topping
A Computationally Efficient Bayesian Framework for Structural Health Monitoring using Physics-Based Models
C. Papadimitriou1, C. Argyris1 and P. Panetsos2
1Department of Mechanical Engineering, University of Thessaly, Volos, Greece
C. Papadimitriou, C. Argyris, P. Panetsos, "A Computationally Efficient Bayesian Framework for Structural Health Monitoring using Physics-Based Models", in Y. Tsompanakis, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 27, 2015. doi:10.4203/ccp.109.27
Keywords: Bayesian inference, structural health monitoring, damage identification, model reduction, kriging, high performance computing.
A Bayesian inference framework for structural damage identification is presented. Sophisticated structural identification methods, combining vibration information from the sensor network with the theoretical information built into a high-fidelity finite element model for simulating structural behaviour, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of the damage. The methodology for damage detection combines the information contained in a set of measurement modal data with the information provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in the structure. The computational challenges encountered in Bayesian tools for structural damage identification are addressed. Simulated modal data from the Metsovo Bridge are used to validate the effectiveness of the methodology.
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