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.

这项研究旨在开发新的公式来预测再生粗骨料（RCA）混凝土的拉伸，压缩和弯曲强度。为此，总共使用了1348个可用的实验结果。帝国主义竞争算法（ICA）用于根据水，水泥，RCA，天然粗骨料（NCA）和天然细骨料（NFA）的含量查找方程式。另外，当使用不同的RCA含量时，在抗弯强度，抗张强度和抗压强度之间建立了一些关系。为了提供用于估计压缩，弯曲和拉伸能力的适当方程式，首先使用影响算法效率的最小冗余最大相关性算法来考虑特征。然后，使用多层感知器（MLP）网络选择用于估计每个参数的适当数量的功能。所得结果表明，在估算抗压强度，抗弯强度和抗拉强度时，所提议公式的平均绝对误差分别为约0.54、0.36和0.48。另外，RCA对混凝土的机械性能有很大的影响，RCA含量应在用于预测混凝土机械特性的公式中考虑。