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C. Papadimitriou¹, C. Argyris¹ and P. Panetsos²

¹Department of Mechanical Engineering, University of Thessaly, Volos, Greece
²Egnatia Odos S.A., Capital Maintenance Department, Thermi, Greece

doi:10.4203/ccp.109.27

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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.

Summary

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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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 109 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 Paper 27 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 ²Egnatia Odos S.A., Capital Maintenance Department, Thermi, Greece doi:10.4203/ccp.109.27 purchase the full-text of this paper Full Bibliographic Reference for this paper 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. Summary 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. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £50 +P&P)
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