A New Optimal Correlation for Behavior factor of EBFs under Near-fault Earthquakes using Artificial Intelligence Models
Subject Areas : Structural EngineeringSeyed Abdonnabi Razavi 1 , Navid Siahpolo 2
1 - Department of Civil Engineering, Abadan Branch, Islamic Azad University, Abadan, Iran
2 - Department of Civil Engineering, Institute for Higher Education ACECR, Khouzestan, Iran
Keywords: Particle Swarm Optimization (PSO), Simulated Annealing (SA), Performance Levels, Artificial Intelligence Models, Behavior factor,
Abstract :
Behavior factor of the structures is a coefficient that includes the inelastic performance of the structure and indicates the hidden resistance of the structure in the inelastic stage. In most seismic codes, this coefficient is merely dependent on the type of lateral resistance system and is introduced with a fixed number. However, there is a relationship between the behavior factor, ductility (performance level), structural geometric properties, and type of earthquake (near and far). In this paper, a new optimal correlation is attempted to predict the behavior factor (q) of EBF steel frames, under near-fault earthquakes, using Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. For this purpose, a databank consists of 12960 data created. To establishing different geometrical properties of models, 3-,6-, 9-, 12-, 15, 20- stories steel EBF frames considered with 3 different types of link beam, 3 different types of column stiffness and 3 different types of brace slenderness. Using nonlinear time history under 20 near-fault earthquake, all models analyzed to reach 4 different performance level. data were used as training data of the Artificial Intelligence Models. Results shows the high accuracy of proposed correlation, established by PSO algorithm. The results of the correlation between the studied algorithms show more accuracy in the relations produced than the previous algorithms and confirm the significance of the governing relations.