# A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
###### Abstract
In this sense, most studies on the use of RCA in the manufacture of concrete show that the incorporation of the coarse fraction of RCA reduces the compressive strength values [20], although in some cases the values of the reference concrete are maintained [21]. [PERSON] et al. [20] found that the incorporation of 100% coarse RCA reduces compressive strength by approximately 50%. [PERSON] et al. [22] found no differences if the percentage of substitution of NA for RCA was less than 30%. In this context, several authors have demonstrated the usefulness of RCA to obtain concretes good enough for construction purposes, but the heterogeneity of the materials used, and the diversity of the variables considered to estimate the compressive strength make it difficult to create a general way to estimate a priori the compressive strength of the resulting concrete, which complicates the generalization of the use of this type of concrete. So, it is necessary to explore the possibility to obtain good estimates of compressive strength without having to make individual tests for each one of the possible combinations, type of constituent and range of the physical properties of the materials used. In this way, Artificial Intelligence (AI) methods have been shown to be useful in this and other fields. AI methods are useful to predict concrete parameters and they have been widely used in the literature. Recently, Artificial Neural Networks (ANN) have been used for estimating compressive strength of concrete [23; 24] as well as other properties, since it is a powerful method for dealing with multivariable problems and generating easy-to-use models, even when the number of inputs is large. Artificial intelligence methods have been shown to be more accurate than multiple regression models (MLR) in predicting the compressive strength of concrete. For example, [PERSON] et al. [25] have recently proposed an MLR to predict the 28-day compressive strength, taking into account the quantity of cement, natural fine aggregates, coarse recycled concrete aggregates, water, water-to-cement ratio, and the following aggregate properties: water absorption, specific gravity, aggregate impact value and aggregate abrasion value, finding R\({}^{2}\) values less than 0.55 in the training phase and less than 0.75 in the test phase, which highlights the invalidity of the MLR method to predict the mechanical properties of concretes. These same authors also proposed an ANN model with a better accuracy than the MLR model. The R\({}^{2}\) value of the ANN models was more than 0.8 in the training phase and more than 0.9 in the test phase, which shows that the ANN is a good alternative to predict the mechanical properties of RCA concrete. [PERSON] et al. (2020) compared the accuracy of obtaining the compressive strength of silica-fume concrete with Artificial Neural Networks (ANN) and Fuzzy Logic (FL). They used 48 different samples with four water-to-cement ratios, three cement dosages and three partial silica-fume replacement ratios, obtaining better results with ANN than with FL. Nevertheless, they concluded that ANN and FL can be alternative methods to predict compressive strength, since the number of samples as well as the number of input parameters used were small. In a similar way, [PERSON] and [PERSON] (2020) compared ANN and Genetic Programming (GEP) for predicting split tensile strength and percentage of water absorption of concretes containing TiO\({}_{2}\) nanoparticles. They used 144 samples with 16 mixture proportions and cement content, nanoparticle content, aggregate type, water content, amount of superplasticizer, type of curing medium and age of curing were used as input variables. They obtained better results with ANN, but those obtained with GEP were reasonably accurate too, while this a simpler method. In a similar way, [PERSON] (2020) modeled, by means of gene expression programming, the compressive strength of geopolymers produced by Portland cement as an aluminosilicate source.