Heuristic algorithm-based semi-empirical formulas for estimating the compressive strength of the normal and high performance concrete

Abstract It is a big challenge to design mixture proportions of the high-performance concrete due to highly nonhomogeneous relationships and coherent among many components. Although machine learning (ML) algorithms have been employed effectively to solve this problem, they are black box models and do not show an explicit relation between the compressive strength and mixture proportions. In order to overcome this inherent weakness, this paper proposes general semi-empirical formulas involving nondimensionalization and optimisation techniques. The optimisation process employs the Nelder–Mead simplex algorithm and takes into account the behavior of uncertain variables, which may occur in experimental data. Successful compressive strength predictions of five datasets with high accuracy in compared to available ML models indicate that the proposed framework has the universal capacity, which can be used for various datasets. Furthermore, the explicit relation of semi-empirical formulas may be a useful tool for engineers and researchers in this area for the prediction purposes.

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