000013206 001__ 13206
000013206 005__ 20161114160331.0
000013206 04107 $$aeng
000013206 046__ $$k2009-06-22
000013206 100__ $$aYoshida, I.
000013206 24500 $$aData assimilation and reliability estimation of existing rc structure

000013206 24630 $$n2.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013206 260__ $$bNational Technical University of Athens, 2009
000013206 506__ $$arestricted
000013206 520__ $$2eng$$aThe clear line that separates aleatory and epistemic uncertainties does not exist, but it depends on the limit state model we adopt. In the process of reliability estimation of existing structures, epistemic uncertainty should be updated by the observation, but aleatory uncertainty should not. We should pay special attention to the difference of epistemic and aleatory uncertainty in reliability estimation of an existing structure. Reliability estimation of existing structures is however more difficult than that of newly constructed structures because probability density function (PDF) of the updated model parameters are not expressed in a simple form when a nonlinear observation equation or nonGaussian variable is involved. Monte Carlo (MC) based methods for non-linear filtering or Bayesian updating techniques have been developed since 1990's. This technique is applied to many fields recently such as space physics, geophysics, medical sciences and so on. From the standpoint of inverse problem, it is known as data assimilation. Though most applications with data assimilation by SMCS concentrate on estimation of model parameters, the methodology can be extended to reliability estimation quite easily. A new framework of reliability estimation of an existing structure is proposed based on SMCS in this study. The proposed method is demonstrated through a numerical example of reliability analysis as to deteriorating RC structures. Corrosion of rebar due to chloride attack is a major mechanism for the deterioration of RC structures. In the numerical example, the uncertainty types, aleatory and epistemic, are discussed. It shows the reliability estimation with epistemic uncertainty assumption gives larger updating magnitude of limit state probabilities.

000013206 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013206 653__ $$aSequential Monte Carlo, particle filter, Reinforced Concrete, Degrading, Inspection, probability Abstract. The clear line that separates aleatory and epistemic uncertainties does not exist, but it depends on the limit state model we adopt. In the process of reliability estimation of existing structures, epistemic uncertainty should be updated by the observation, but aleatory uncertainty should not. We should pay special attention to the difference of epistemic and aleatory uncertainty in reliability estimation of an existing structure. Reliability estimation of existing structures is however more difficult than that of newly constructed structures because probability density function (PDF) of the updated model parameters are not expressed in a simple form when a nonlinear observation equation or nonGaussian variable is involved. Monte Carlo (MC) based methods for non-linear filtering or Bayesian updating techniques have been developed since 1990's. This technique is applied to many fields recently such as space physics, geophysics, medical sciences and so on. From the standpoint of inverse problem, it is known as data assimilation. Though most applications with data assimilation by SMCS concentrate on estimation of model parameters, the methodology can be extended to reliability estimation quite easily. A new framework of reliability estimation of an existing structure is proposed based on SMCS in this study. The proposed method is demonstrated through a numerical example of reliability analysis as to deteriorating RC structures. Corrosion of rebar due to chloride attack is a major mechanism for the deterioration of RC structures. In the numerical example, the uncertainty types, aleatory and epistemic, are discussed. It shows the reliability estimation with epistemic uncertainty assumption gives larger updating magnitude of limit state probabilities.

000013206 7112_ $$aCOMPDYN 2009 - 2nd International Thematic Conference$$cIsland of Rhodes (GR)$$d2009-06-22 / 2009-06-24$$gCOMPDYN2009
000013206 720__ $$aYoshida, I.
000013206 8560_ $$ffischerc@itam.cas.cz
000013206 8564_ $$s476871$$uhttp://invenio.itam.cas.cz/record/13206/files/CD281.pdf$$yOriginal version of the author's contribution as presented on CD, section: Statistical and probabilistic methods in computational mechanics to treat aleatory and epistemic uncertainties in structural and/or geotechnical systems and their loading environment - i (MS).
000013206 962__ $$r13074
000013206 980__ $$aPAPER