This paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to provide explicit mathematical equations for estimating SL. The experimental data set contains five input variables, including the water-cement ratio (W/C), water (W), cement (C), river sand (Sa), and Bida Natural Gravel (BNG) used for the estimation of SL. Three common statistical indices, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the accuracy of the derived equations. The statistical indices revealed that the GEP formula (R=0.976, RMSE=19.143, and MAE=15.113) was more accurate than the MARS equation (R=0.962, RMSE=23.748, and MAE=16.795). However, the application of MARS, due to its simple regression equation for estimating SL, is more convenient for practical purposes than the complex formulation of GEP
