Abstract: The compressive strength and slump of concrete have highly nonlinear functions relative to given components. The importance of predicting these properties for researchers is greatly diagnosed in developing constructional technologies. Such capacities should be progressed to decrease the cost of expensive experiments and enhance the measurements’ accuracy. This study aims to develop a Radial Basis Function Neural Network (RBFNN) to model the hardness features of High-Performance Concrete (HPC) mixtures. In this function, optimizing the predicting process via RBFNN will be aimed to be accurate, as the aim of this research, conducted with metaheuristic approaches of Henry gas solubility optimization (HGSO) and Multiverse Optimizer (MVO). The training phase of models RBHG and RBMV was performed by the dataset of 181 HPC mixtures having fly ash and superplasticizer. Regarding the results of hybrid models, the MVO had more correlation between the predicted and observed compressive strength and slump values than HGSO in the R2 index. The RMSE of RBMV (3.7 mm) was obtained 43.2 percent lower than that of RBHG (5.3 mm) in the appraising slump of HPC samples, while, for compressive strength, RMSE was 3.66 MPa and 5 MPa for RBMV and RBHG respectively. Moreover, to appraise slump flow rates, the R2 correlation rate for RBHG was computed at 96.86 % while 98.25 % for RBMV in the training phase, with a 33.30% difference. Generally, both hybrid models prospered in doing assigned tasks of modeling the hardness properties of HPC samples.
