Probabilistic damage assessment of a seven-story reinforced concrete shear wall building by means of bayesian fe model updating

Abstract eng:
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.

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