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S.A. Faroz, N.N. Pujari, S. Ghosh, "A Bayesian Markov Chain Monte Carlo Approach for the Estimation of Corrosion in Reinforced Concrete Structures", in , (Editors), "Proceedings of the Twelfth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 150, 2014. doi:10.4203/ccp.106.150

Keywords: corrosion, reinforced concrete, steel loss, Bayesian updating, MCMC, Markov chain..

Summary

Reinforced concrete structures degrade primarily as a result of corrosion-induced damage, mostly as a consequence of the loss of steel rebar volume. Therefore, the prediction of time-varying damage resulting from corrosion is important in assessing the residual life of a structure and making decisions on maintenance or repair. Existing models of prediction fail to provide realistic estimates of the steel loss over time. This paper presents a methodology for a probabilistic evaluation of the time-dependent corrosion loss in rebars. A Bayesian updating approach combined with a Markov Chain Monte Carlo simulation is adopted here. This gives the advantage of modelling the corrosion parameters based on measured data combined with some 'prior' or existing understanding of these parameters. Experimental results are compiled from the reported literature and the proposed probabilistic model is validated against these data to show its effectiveness, over the existing models. Sensitivity of the results to critical uncertainty parameters is presented.

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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: 106 PROCEEDINGS OF THE TWELFTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: Paper 150 A Bayesian Markov Chain Monte Carlo Approach for the Estimation of Corrosion in Reinforced Concrete Structures S.A. Faroz, N.N. Pujari and S. Ghosh Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India doi:10.4203/ccp.106.150 purchase the full-text of this paper Full Bibliographic Reference for this paper S.A. Faroz, N.N. Pujari, S. Ghosh, "A Bayesian Markov Chain Monte Carlo Approach for the Estimation of Corrosion in Reinforced Concrete Structures", in , (Editors), "Proceedings of the Twelfth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 150, 2014. doi:10.4203/ccp.106.150 Keywords: corrosion, reinforced concrete, steel loss, Bayesian updating, MCMC, Markov chain.. Summary Reinforced concrete structures degrade primarily as a result of corrosion-induced damage, mostly as a consequence of the loss of steel rebar volume. Therefore, the prediction of time-varying damage resulting from corrosion is important in assessing the residual life of a structure and making decisions on maintenance or repair. Existing models of prediction fail to provide realistic estimates of the steel loss over time. This paper presents a methodology for a probabilistic evaluation of the time-dependent corrosion loss in rebars. A Bayesian updating approach combined with a Markov Chain Monte Carlo simulation is adopted here. This gives the advantage of modelling the corrosion parameters based on measured data combined with some 'prior' or existing understanding of these parameters. Experimental results are compiled from the reported literature and the proposed probabilistic model is validated against these data to show its effectiveness, over the existing models. Sensitivity of the results to critical uncertainty parameters is presented. 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 £65 +P&P)
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