Abstract
The effect of fire on the reinforced concrete structures' lateral load-bearing systems is not well established in literature and design codes. Since in case of an earthquake this influence can be crucial, in this study we aimed to predict post-fire residual compressive strength of concrete in shear walls using artificial neural network (ANN) models. The network parameters were fine-tuned using the bat optimization metaheuristic algorithm. The accuracy of the BAT-based ANN models was validated by comparing their predictions with the particle swarm optimization (PSO) algorithm-based ANNs and multiple linear regression models. The results for BAT-trained networks revealed a mean squared error (MSE) of 4.881 MPa, and prediction-target correlation of 0.987 on testing data which are more accurate than their PSO-trained counterparts.
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