Performance-based Optimization of Reinforced Concrete Frames by Means of Meta-Heuristic Algorithms & Neural Network

The mean objective of performance based optimization of reinforced concrete frames (RC) is to reduce the cost of construction by requiring the satisfaction of the inter-story drifts and rotation of the plastic joints of the members. In this research, two 3 & 6 stories RC performance-based optimized by Particle Swarm (PSO), Enhanced Colliding Bodies (ECBO), firefly Algorithm (FA),Ants Colony (ACO) and Bat (BAT) meta-heuristic algorithms, then compare results with together. Optimization of RC is much complicated than Steel frames, because different dimensions of members & configuration of reinforcing. Due to the high cost of seismic performance evaluation of structures, in this research, neural networks used to increase the computational speed & reduce the operating time. Numerical results show the proper performance of the ECBO in comparison with other meta-heuristic algorithms.Also, the results of different algorithms do not show much difference.For further evaluation of the results, it is recommended to Calculate its Collapse Margin Ratios.