Research Article Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete Amir Andalib , 1 Babak Aminnejad , 2 and Alireza Lork 3 1 Department of Civil Engineering, Kish International Branch, Islamic Azad University, Kish Island, Iran 2 Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran 3 Department of Civil Engineering, Safadasht Branch, Islamic Azad University, Tehran, Iran Correspondence should be addressed to Babak Aminnejad; aminnejad@riau.ac.ir Received 10 September 2021; Revised 3 January 2022; Accepted 18 January 2022; Published 17 February 2022 Academic Editor: Cheng-Jian Lin Copyright © 2022 Amir Andalib et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Ever since their presentation in the late 80s, self-compacting concrete (SCC) has been well received by researchers. SCC can flow under their weight and exhibit high workability. Nonetheless, their nonlinear behavior has made the prediction of their mix properties more demanding. Furthermore, the complex relationship between mixed proportions and rheological and mechanical properties of SCC renders their behavior prediction challenging. Soft computing approaches have been shown to optimize and reduce uncertainties, and therefore in this paper, we aim to address these challenges by employing artificial neural network (ANN) models optimized using the grey wolf optimizer (GWO) algorithm. e optimized model proved to be more accurate than genetic algorithms and multiple linear regression models. e results indicate that the four most influential parameters on the com- pressive strength of SCC are the cement content, ground granulated blast furnace slag, rice husk ash, and fly ash. 1. Introduction A proper self-compacting concrete mix design requires balancing two conflicting objectives: deformability and stability, that is, acceptable rheological behavior and ap- propriate mechanical characteristics. So, the proportions of available materials, minerals, and admixtures must be considered. e optimum balance of coarse and fine ag- gregates and chemical admixtures ensures the greater co- hesiveness of self-compacting concrete. External variations such as changes in the production process of cement and mineral additives and the type of aggregates can trigger significant variations in the properties of fresh self-com- pacting concrete. To minimize such external variants, the use of industrial derivatives and mineral additives in the manufacturing of lightweight self-compacting concrete has been the focus of many scientists [1]. Due to the complex relationship between mixed pro- portions and rheological and mechanical properties of SCC, researchers have proposed numerous treatments in the literature. Some researchers have used statistical models such as linear regression to predict the compressive strength of SCC [2], while others used numerical methods to this end [3]. Among the different approaches suggested, soft com- puting-based methods have reported promising results [4]. ey include methods such as random kitchen sink algo- rithm [5], swarm optimization algorithm [6], and back- propagation algorithm [7]. Zhang et al. developed a random forest model based on the beetle antenna search algorithm to predict the com- pressive strength of SCC [8], while Nehdi et al. utilized a neural network approach to this end [2]. ANNs have shown promising results in the engineering domain [9, 10] and are widely employed by researchers to predict concrete’s compressive strength [11, 12]. Prasad et al. employed ANNs to predict the compressive strength of high-performance SCC [13], and Siddique et al. used ANNs for SCC containing fly ash [14]. Uysal and Tanyildizi used ANNs to predict the compressive strength of SCC mixtures with mineral addi- tives [15]. A novel approach based on the proposed nor- malization method for artificial neural networks was employed by Asteris et al. to determine the compressive Hindawi Applied Computational Intelligence and So Computing Volume 2022, Article ID 9887803, 17 pages https://doi.org/10.1155/2022/9887803