000013222 001__ 13222
000013222 005__ 20161114160332.0
000013222 04107 $$aeng
000013222 046__ $$k2009-06-22
000013222 100__ $$aLombaert, G.
000013222 24500 $$aProbabilistic damage assessment of a seven-story reinforced concrete shear wall building by means of bayesian fe model updating

000013222 24630 $$n2.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013222 260__ $$bNational Technical University of Athens, 2009
000013222 506__ $$arestricted
000013222 520__ $$2eng$$aLocalizing and quantifying potential damage in large and complex structures is one of the most challenging problems in structural health monitoring. Vibration-based finite element (FE) model updating has proven to be a powerful methodology to identify (i.e., detect, localize and quantify) structural damage. However, due to the presence of uncertainties on the identified modal parameters used in model updating, probabilistic damage identification methods are preferable. Such methods must allow accounting for all pertinent sources of uncertainty and must express the damage identification results in probabilistic terms. In this paper, Bayesian FE model updating is applied for the damage identification of a full-scale seven-story reinforced concrete building slice tested on the UCSD-NEES shake table. The shake table tests were designed so as to damage the building progressively through a sequence of historical earthquake records reproduced on the shake table. In this study, modal parameters identified based on ambient vibration data acquired at various levels of damage are used to identify the existing damage by means of FE model updating. For this purpose, the FE model of the structure is divided into a number of substructures. Damage is identified and localised as a decrease of the effective stiffness in these substructures. The Bayesian inference scheme is used to compute the posterior joint probability density of the stiffness in the substructures. The maximum a posteriori (MAP) estimate of the stiffness has a low value for the substructures where the actual damage was observed in the test structure. The variance of the identified values tends to be lower for those substructures where the most severe decrease in stiffness is identified.

000013222 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013222 653__ $$aProbabilistic Model Updating, Bayesian Inference, Vibration-based Testing. Abstract. Localizing and quantifying potential damage in large and complex structures is one of the most challenging problems in structural health monitoring. Vibration-based finite element (FE) model updating has proven to be a powerful methodology to identify (i.e., detect, localize and quantify) structural damage. However, due to the presence of uncertainties on the identified modal parameters used in model updating, probabilistic damage identification methods are preferable. Such methods must allow accounting for all pertinent sources of uncertainty and must express the damage identification results in probabilistic terms. In this paper, Bayesian FE model updating is applied for the damage identification of a full-scale seven-story reinforced concrete building slice tested on the UCSD-NEES shake table. The shake table tests were designed so as to damage the building progressively through a sequence of historical earthquake records reproduced on the shake table. In this study, modal parameters identified based on ambient vibration data acquired at various levels of damage are used to identify the existing damage by means of FE model updating. For this purpose, the FE model of the structure is divided into a number of substructures. Damage is identified and localised as a decrease of the effective stiffness in these substructures. The Bayesian inference scheme is used to compute the posterior joint probability density of the stiffness in the substructures. The maximum a posteriori (MAP) estimate of the stiffness has a low value for the substructures where the actual damage was observed in the test structure. The variance of the identified values tends to be lower for those substructures where the most severe decrease in stiffness is identified. 1

000013222 7112_ $$aCOMPDYN 2009 - 2nd International Thematic Conference$$cIsland of Rhodes (GR)$$d2009-06-22 / 2009-06-24$$gCOMPDYN2009
000013222 720__ $$aLombaert, G.$$iMoaveni, B.$$iHe, X.$$iConte J., P.
000013222 8560_ $$ffischerc@itam.cas.cz
000013222 8564_ $$s1394453$$uhttps://invenio.itam.cas.cz/record/13222/files/CD311.pdf$$yOriginal version of the author's contribution as presented on CD, section: Identification methods in structural dynamics - i (MS).
000013222 962__ $$r13074
000013222 980__ $$aPAPER