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This paper proposes the ductility demands control of reinforced concrete frames under criticalsuccessive earthquakes using evaluation of behavior factors (R factors). The influence of RC frameperiods, PGA and magnitude of mainshocks and aftershocks is also taken into account by 10 training ideal artificial neural network (ANN) and proposing the empirical equations. Firstly, 2D RC frames are implemented in Opensees and then evaluated under as-recorded critical single and successive scenario. R factors are calculated and compared for single and successive cases. It is found that the sequences of critical records decrease R factors and capacity of RC frames about 18% and 30%, respectively. Despite what is necessitated in the seismic design codes, proposing a constant value as R factor for whole RC structure especially under single scenarios cannot lead to proper design of structures. Hence, the idealized multilayer ANNs employed to generate the empirical charts for evaluation of R factors. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology using wavelet packet transform, principal component analysis, and neural networks in order to generate artificial critical aftershock accelerograms which are compatible with the response spectra. This procedure uses the learning abilities of neural networks, principal component analysis as a dimension reduction technique, and decomposing capabilities of wavelet packet transform on consecutive earthquakes. In fact, the proposed methodology consists of two steps and expands the knowledge of the inverse mapping from mainshock response spectrum to aftershock response spectrum and aftershock response spectrum to wavelet packet transform coefficients of the aftershocks. This procedure results in a stochastic ensemble of response spectra of aftershock (first step) and corresponding wavelet packet transform coefficients (second step) which are then used to generate the aftershocks through applying the inverse wavelet packet transform. Finally, in order to demonstrate the effectiveness of the proposed method, three examples are presented in which recorded critical successive ground motions are used to train and test the neural networks. [ABSTRACT FROM AUTHOR]