Abstract. A procedure for updating the Park-Ang damage index of reinforced concrete building under near-fault ground motion is proposed. Rather than developing a new damage model, a correction term is added to the existing damage model within the Bayesian framework. The correction term is described as a linear function of the variation of sti ness of structures, which is a more consistent indicator of predicting the level of damage. The Bayesian method is an e ective approach when new data become available. The reinforced concrete building damage data during past near-fault pulse-like earthquakes were used in updating the damage model. The proposed damage index is conceptually simple and realistic.

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A probabilistic seismic demand model that relates ground motion intensity measures (IMs) to the structural demand measures is a useful tool for reliability analysis of structures. It is common to utilize the scalar seismic parameters or a vector of a few seismic parameters to reveal ground motion uncertainty. However, for the qualification of an IM for representing the ground motion uncertainty, a larger vector of greater seismic component is required. This study aims to use more parameters as vector IMs in the demand model to achieve better estimation of the ground motion uncertainty. In this study, three-layer feed forward neural network was used to predict the seismic demand model of the mid-rise reinforced concrete buildings for pulse-like ground motions. The results indicate that due to the complexity of the relationship between seismic response of structures and seismic intensity parameters, using artificial neural networks method is more suitable than numerical methods to show uncertainties.

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