Examining the properties of High-Performance Concrete (HPC) has been a big challenge due to the highly heterogeneous relationships and coherence among several constituents. The employment of silica fume and fly ash as eco-friendly components in mixtures benefits the concrete to improve its physical features. Although machine learning approaches are utilized broadly in many studies solitarily to estimate the mechanical features of concrete, causing to reduce accuracy and lift the cost and complexities of computational networks. Consequently, current research aims to develop a Radial Basis Function Neural Network (RBFNN) integrating with optimization algorithms in order to precisely model the mechanical characteristics of HPC mixtures including compressive strength (CS) and slump (SL). Feeding the dataset of HPC samples to hybrid models will result to reproduce the given CS and SL factors simultaneously. The results of the models showed that the maximum rate of correlation between estimated values and measured ones was obtained at 98.3% while the minimum rate of RMSE was calculated at 3.684 mm (and MPa) in the testing phase. Employing such soft-oriented approaches has been benefiting us to reduce costs and increase the result accuracy.
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