# Application of Three Metaheuristic Techniques in Simulation of Concrete Slump
## 1 Introduction  
As a fundamental material in almost every civil engineering activity, concrete is a widely-used man-made mixture, composed of some basic elements (like cement and water) and additional materials. Other than the reasonable compressive strength of concrete, flowability is another determinant factor which helps workers to form concrete into any desired shape [1]. Up to now, considering the various properties of concrete (e.g., durability and early age strength), different versions of this material (e.g., ready mix (RM) and self-compacting concretes) have been produced. Out of those, high performance concrete (HPC) is special; it is famous for its workability, which is mainly influenced by the ratio of finer particles [2]. Slump is a determinant factor of concrete which directly contributes to the workability of a mixture [3]. Hence, producing a mixture with a proper slump is significant. On the other hand, there are various parameters like cement/water ratio that considerably affect the slump. Therefore, indirect measurement of slump has received growing attention for analyzing the effect of these ingredients. Among the diverse methods suggested for evaluating and predicting slump, intelligent models likeartificial neural networks (ANNs) have shown promise. ANNs are capable predictive tools that mimic the biological neural systems [4; 5; 6; 7; 8]. The main advantage of this model in comparison with traditional predictive models (like linear regression) lies in the capability of analyzing the non-linear relationship between independent and dependent variables (slump and effective factors in the present work) [9], which has driven many scholars to employ it for various engineering issues [10; 11; 12; 13]. More specifically, the network tries to adjust the computational parameters pertaining to effective factors through a back-propagation procedure. In this method, the error of the performance is calculated and then, is considered in a back-ward path. This process leads to setting more compatible parameters of a network [14; 15; 16; 17; 18]. Different intelligent models have been used to investigate various characteristics of concrete [19; 20; 21; 22]. In the case of ANNs, [PERSON] et al. [23] successfully used this tool for predicting the slump and compressive strength of high strength concrete. [PERSON] [24] investigated the effect of slump influential factors, including superplasticizer-binder ratio (SP/B), water/binder ratio (w/b), and water content using ANNs. [PERSON] [25] developed an ANN-based methodology for simulating the slump of fly ash and slag concrete (FSC). Additionally, the relationship between the slump and concrete component was examined by response trace plots.  
Moreover, a number of researchers have used hybrid evolutionary algorithms in different fields [26; 27; 28; 29]. For slump modelling, [PERSON] et al. [30] established a geometric semantic genetic programming (GSGP) for predicting recycled concrete slump. [PERSON] et al. [31] coupled ANN with a genetic algorithm (GA) for estimating the slump of RM concrete. With respect to the obtained root mean square errors (RMSEs) of 3.4634 and 2.4994, as well as regressions of 0.9605 and 0.9791, respectively, for the typical ANN and GA-ANN models, they concluded that the proposed hybrid model was suitable for the purpose of slump prediction. Likewise, [PERSON] et al. [32] used a parallel hyper-cubic gene expression programming (GEP) for estimating the slump flow of HPC concrete. Their findings showed that the proposed hybrid model outperformed typical GEP approach. As stated above, despite the wide application of typical predictive methods for slump prediction issues, few studies have conducted optimization of these networks using metaheuristic approaches. Hence, the focal objective of this paper is to present a novel optimization of an ANN, namely ant lion optimization (ALO), used for fine-tuning the computational parameters in concrete slump simulation.