Determining post-fire residual compressive strength of reinforced concrete shear walls using the BAT algorithm. (August 2021)

• Record Type: Journal Article
• Authors: Shadbahr, Elham; Aminnejad, Babak; Lork, Alireza
• Is Part Of: Structures. Volume 32(2021)
• Page Start: 651
• Page End: 661
• Publication Date: 2021-08
• Subjects: Fire damage -- Reinforced concrete shear wall -- Artificial neural networks -- Bat algorithm -- Particle swarm optimization; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2021.03.002
• Languages: English
• ISSNs: 2352-0124

Abstract

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.