In this study, the idea of recycling the concrete wastes and reuse of them for reproduction of green concrete has been presented. Thus, we have tried to study mechanical parameters using recycled aggregate concrete. For this purpose, three mix designs including natural, recycled and recycled fiber concrete were tested. Moreover, at the end of the paper, estimation of compressive strength using ANN methods has been presented. Based on the results, the recycled concrete and recycled fiber concrete with the proposed mix design have a high compressive strength, and due to relatively high porosity of the recycled aggregate concrete, its density has decreased by 2.48% and its water absorption increased by 54% compared to the natural concrete. Two artificial intelligence methods of ANN and SVM benefit from a quite equal coefficient of consistency, and the results of 124 test specimens with the results obtained from SVM are in a better agreement. Finally, two artificial intelligence methods were compared with the MLR using K-fold cross-validation, indicating superior performance of the artificial intelligence.

在这项研究中，提出了回收混凝土废料并将其再利用以生产绿色混凝土的想法。因此，我们试图研究使用再生骨料混凝土的机械参数。为此，测试了三种混合设计，包括天然，再生和再生纤维混凝土。此外，在本文的最后，提出了使用ANN方法估算抗压强度的方法。根据结果​​，采用建议的配合设计的再生混凝土和再生纤维混凝土具有较高的抗压强度，并且由于再生集料混凝土的孔隙率较高，其密度降低了2.48％，吸水率提高了54％与天然混凝土相比 ANN和SVM的两种人工智能方法都受益于相当相等的一致性系数，124个试样的结果与SVM的结果吻合得更好。最后，将两种人工智能方法与MLR进行了比较：K折交叉验证，表明了人工智能的卓越性能。