Simulation of steel-concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework

In this research, the principal purpose is investigating the performance of structural elements significantly degrades at elevated temperatures. Steel-concrete composite floor systems are one of the most relevant components in building construction in which fire-induced problems directly damage their performance. The purpose of this study is on employing analytical intelligence technique to predict two major structural characteristics of the steel-concrete composite floor system. Two intelligent methods, ELM-PSO and ELM-GWO, were used as a multi-combination AI method to predict the shear and tensile response of these composite floor systems at high temperature. Accordingly, authenticated data on monotonic loading response of this steel-concrete composite floor system in different heat stages had been employed from different literatures. The results show that ELM-GWO technique presented the best prediction of split-tensile load, and the ELM-GWO provided the best estimation of slip value. By giving each prediction equations, the best equations were proposed. As a result, employing ELM-GWO, successfully presented reliable and decisive results and proved the proficiency of the techniques.