جستجوی مقالات مرتبط با کلیدواژه "الگوریتم خفاش bat" در نشریات گروه "عمران"

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.