Behavior factor prediction equations for reinforced concrete frames under critical mainshock-aftershock sequences using artificial neural networks

This paper proposes the ductility demands control of reinforced concrete frames under critical-successive 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.