Analytical Assessment of the Structural Behavior of a Specific Composite Floor System at Elevated Temperatures Using a Newly Developed Hybrid Intelligence Method

The aim of this paper is to study the performance a composite floor system at different heat stages using artificial intelligence derive sustainable design and select most critical factors for elevated temperatures. In system, load bearing due action between steel concrete materials which achieved by shear connectors. Although connectors play an important role in transferring force from profile, if exposed high temperature conditions excessive deformations may reduce shear-bearing capacity system. Therefore, paper, slip response angle evaluated techniques determine during Accordingly, authenticated experimental data on monotonic loading steel-concrete were employed analytical assessment. Moreover, neural network was developed with fuzzy (ANFIS) optimized genetic algorithm (GA) particle swarm optimization (PSO), namely ANFIS-PSO-GA (ANPG) method. addition, results ANPG method compared those extreme learning machine (ELM) radial basis function (RBFN) mechanical geometrical properties temperatures included dataset. Based results, although behavior accurately predicted three methods, RBFN methods represented accurate values split-tensile prediction, respectively. numerical since had rational relationship parameters, it dramatically predictable. determined as affecting