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Purpose This paper aims to predict the fire resistance of steel-reinforced concrete columns by application of the genetic algorithm. Design/methodology/approach In total, 11 effective parameters are considered including mechanical and geometrical properties of columns and loading values as input parameters and the duration of concrete resistance at elevated temperatures as the output parameter. Then, experimental data of several studies – with extensive ranges – are collected and divided into two categories. Findings Using the first set of the data along with the gene expression programming (GEP), the fire resistance predictive model of steel-reinforced concrete (SRC) composite columns is presented. By application of the second category, evaluation and validation of the proposed model are investigated as well, and the correspondent time-temperature diagrams are derived. Originality/value The relative error of 10% and the R coefficient of 0.9 for the predicted model are among the highlighted results of this validation. Based on the statistical errors, a fair agreement exists between the experimental data and predicted values, indicating the appropriate performance of the proposed GEP model for fire resistance prediction of SRC columns.

This study investigates the post-fire performance of steel reinforced concrete composite (SRC) columns used in building construction using experimental compression tests. Four conventional steel sections as I-shaped, Cross, Box, and Plus were considered as the steel core. For fire loading, all fabricated columns were subjected to five target temperatures, including 25°, 250°, 500°, 700°, and 900°C, and then the cooling phase of the columns was done under natural conditions. Then using pressure jacks, the compressive behavior including strength and axial deformation of each of them was measured, and force-deformation curves were plotted and investigated for each SRC column. The results of these tests showed that the compressive strength and the elasticity modulus of the columns decrease at higher temperatures. Also, the effect of the steel core was examined on the compressive strength. Among the tested sections, SRC columns with the Box-steel core showed a more recovery in its compressive strength and elasticity modulus, and hence, its performance was better than SRC columns with the other steel cores. Moreover, SRC columns with the Plus-steel core indicated the weakest compressive strength and elasticity modulus. Finally, some equations were proposed for the prediction of the compressive strength and elasticity modulus of SRC columns at different temperatures by applying gene expression programming (GEP modeling) to the results.

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