# A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
###### Abstract
The main factors considered were NaOH concentration, water-glass-to-NaOH-weight ratio, alkali-activator-to-cement-weight ratio, oven curing temperature, oven curing time, and water curing regime, with each one of them at four levels. They obtained good results. [PERSON] et al. (2019) used genetic programming with geometric, semantic, genetic operators to predict compressive strength of high-performance concrete using quantities of cement, fly ashes, blast furnace slag, water, superplasticizer, coarse aggregate, fine aggregate, and age of testing as input variables, outperforming the results previously obtained with standard genetic programing. [PERSON] et al. (2020) predicted compressive strength, modulus of elasticity, and splitting tensile strength of recycled concrete considering the recycled percentage, the quality of the recycled aggregates, and the production method. They analyzed 1831 samples obtained from 81 studies by means of multivariable regression and genetic programming, obtaining good enough results in comparison with those previously identified. Finally, [PERSON] et al. (2020) used gene expression programming for predicting mechanical properties of recycled aggregate concrete. As well as the above-mentioned authors, many samples were extracted from the literature. They proposed expressions for predicting compressive strength, elastic modulus, flexural strength, and splitting tensile strength, obtaining results comparable with the previously known models. Recently, ANN have been used for estimating compressive strength of concrete ([PERSON] and [PERSON], 2020; [PERSON] et al., 2020) as well as other properties, since it is a powerful method for dealing with multivariable problems and giving easy-to-use models, even when the number of inputs is large. So, [PERSON] et al. (2020) tried to predict the compressive strength of concrete containing metkao-line, extracting data from the literature in a total of 239 samples (in two sets of 105 and 134 samples, respectively). Results showed MSE of 0.002 and 0.0017, and their predicted data was within \(\pm\)20% of the sample data. In the same way, [PERSON] and [PERSON] (2020) proposed a model estimating compressive strength of concrete by means of neural networks. They used 75 samples with various water-to-cement ratios, and their compressive strength was determined at different ages of 7, 20 and 32 days. They only used water-to-cement ratio, age, and number of freeze/thaw cycles as input variables, obtaining a coefficient of determination of R\({}^{2}\) > 0.87 and maintaining predicted data in \(\pm\)15% of sample data. Table 1 summarize the recent approaches to predict concrete's parameters by means of AI techniques. As Table 1 shows, there is a variety of papers searching for estimating different parameters of concretes based on their constituents (especially compressive strength), but there is no consensus about the number of subjacent input parameters to be considered, neither the number of previous tests to be carried out in order to obtain good predictions. Thus, there is still a question to be answered: whether there is a possibility of obtaining good predictions even when using a heterogeneous set of samples and a wide range of input values. In this way, and according to the above-mentioned literature, several works study the usefulness of RCA as a good alternative to replace NA. Nevertheless, the variety of kind of materials used, and the wide range of their physical properties' values, make it difficult to obtain an accurate prediction of the compressive strength without the necessity of making individual and costly tests. This paper is focused on searching for an easy way to obtain a priori a prediction of the compressive strength of concretes made using RCA, even though the kind of constituents are heterogeneous and difficult to be classified, with the aim of providing a good working tool to concrete manufacturers.