## 1 Introduction  
Concrete workability and strength are essential characteristics that should be determined and used widely in construction projects [1; 2; 3]. Compressive strength (CS) of concrete denotes the level of uniaxial compressive stress, which refers to the concrete properties of concrete after hardening [1]. The workability of concrete is required for the handling and producing of concrete during the construction. While it affects the cost of construction finalization [1]. Herein, the slump-flow (S) test is one of the tests that almost uses to estimate the concrete workability. Herein, with increasing the engineering applications of concrete, the self-compacting concrete (SCC) is developing to use with easy replacement in a narrow spacing of steel of reinforcement concrete [2; 4; 5]. For that, both SCC characteristics, CS and S, should be accurately estimated. Several studies have investigated the parameters that influence the CS and S values of conventional concrete [6], SCC [4] and high strength concrete (HSC) [7]. [PERSON] et al. [5] summarized the impact of admixture contents of SCC in that fresh and hardened properties. From their literature, it can be concluded that fly ash (FA), silica fume (SF), limestone powder (LP), metakaolin (MK) and water/powder (W/P) ratio are the main factors that can be impacted on the SCC properties. More explanation for the impact of admixture contents in the SCC characteristics can be found in [8; 9; 10]. However, the modeling approaches that can be used to estimate optimal admixture and SCC properties are still limited. Experimental design and regression models are usually utilized to determine the CS and S values of concrete [4; 7]. Currently, different linear and nonlinear regression approaches have been utilized to estimate the SC and/or S values of concrete, including group method of data handling (GMDH), Minimax Probability Machine Regression (MPMR), emotional neural network (ENN), and artificial neural network (ANN). For instance, [PERSON] et al. [11] applied MPMR to predict the CS of concrete, and the results showed that the performance of it is acceptable, coefficient of correlation (R) = 93.5%, to use for estimating the CS values. [PERSON] et al. [12] used ANN to predict the S and CS of SCC, and authors concluded that the ANN is a useful method can be used into predicting SCC properties, with R = 80% and 95% for the S and CS, respectively. Fuzzy logic was also applied to estimate the S and CS of concrete and the results of it showed accurate with an acceptable rate of errors [6]. Deep learning based on ANN was also applied to estimate the S value of SCC, and the results showed the performance of the proposed model could be utilized routinely estimate SCC workability [13]. [PERSON] et al. [14] used ENN to estimate the CS of hardened concrete and the results showed it is robust for predicting of concrete behavior. GMDH was found a great tool can be applied to estimate the CS of hardened concrete and CS based on a core test of concrete [15; 16]. More studies can be found in [17; 18; 19; 20; 1; 2] for using advanced soft computing techniques into detecting the S and CS values of conventional concrete, SCC and HSC. Furthermore, hybrid algorithms have been used to estimate concrete properties. [PERSON] et al. [21] combined ANN with the imperialist competitive algorithm, and they found that that model is applicable to estimate the CS of conventional concrete. [PERSON] et al. [22] compared a hybrid artificial neural network-particle swarm optimization (ANN-PSO) with ANN to estimate the behavior of channel connectors in normal and HSC, and the performance of ANN-PSO was seen better. Optimization algorithm (WOA) was integrated with ANN to predict the CS of conventional concrete, and the results showed that integration was improved the modeling for CS values [23]. A combination between fuzzy radial basis based on ANN with biogeography-based optimization was proposed to estimate the CS of SCC and good performance of CS was obtained from the proposed model [24].