This study aims to develop new formulas to predict the tensile, compressive and flexural strengths of recycled coarse aggregates (RCA) concrete. For this purpose, a total of 1348 available experimental results are used. Imperialist Competitive Algorithm (ICA) is employed to find equations based on the content of water, cement, RCA, natural coarse aggregates (NCA) and natural fine aggregates (NFA). In addition, some relationships are developed between flexural, tensile and compressive strengths when different RCA contents are used. To provide the proper equations for estimating the compression, bending and tension capabilities, first the features are considered using the minimum redundancy maximum relevance algorithm that affects the efficiency of the algorithms. Then, the appropriate number of features for estimating each parameter is selected using the Multi-Layer Perceptron (MLP) network. The obtained results showed that the mean absolute error of the proposed formulas in estimating the compressive, flexural and tensile strengths are about 0.54, 0.36 and 0.48, respectively. Additionally, RCA had a substantial influence on the mechanical properties of concrete, and the RCA content should be considered in the formulas utilized to foresee the mechanical characteristics of concrete.

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