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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]

The current paper introduces a new approach for development of damage index to obtain the maximum damage in the reinforced concrete frames caused by as-recorded single and consecutive earthquakes. To do so, two sets of strong ground motions are selected based on maximum and approximately maximum peak ground acceleration (PGA) from "PEER" and "USGS" centers. Consecutive earthquakes in the first and second groups, not only occurred in similar directions and same stations, but also their real time gaps between successive shocks are less than 10 minutes and 10 days, respectively. In the following, a suite of six concrete moment resisting frames, including 3, 5, 7, 10, 12 and 15 stories, are designed in OpenSees software and analyzed for more than 850 times under two groups of as-recorded strong ground motion records with/without seismic sequences phenomena. The idealized multilayer artificial neural networks, with the least value of Mean Square Error (MSE) and maximum value of regression (R) between outputs and targets were then employed to generate the empirical charts and several correction equations for design utilization. To investigate the effectiveness of the proposed damage index, calibration of the new approach to existing real data (the result of Park-Ang damage index 1985), were conducted. The obtained results show good precision of the developed ANNs-based model in predicting the maximum damage of regular reinforced concrete frames. [ABSTRACT FROM AUTHOR]