# A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
## 2 Materials and Methods  
A database with 177 mixes selected from 15 studies on the effect of incorporating RCA on the compressive strength of concrete were selected (Appendix A). The compressive strength of concrete is the strength of hardened concrete measured from cylindrical specimens (15 \(\times\) 30 cm) or cubic specimens (10 \(\times\) 10 cm) of concrete in a compression machine. The compressive strength of concrete is calculated by dividing the breaking load by the cross-sectional area that resists the load, using the megapascal (MPa) as the unit of measure. The following standards have been used in the referenced studies: ACTM C39 (Standard method for compressive strength of cylindrical concrete specimens), BS 1881-Part 116 (Testing concrete part 116: method for determination of compressive strength of concrete cubes) and EN 12390-3 (Testing hardened concrete--Part 3: compressive strength of test specimens). In accordance with [PERSON] et al. (2019) all RCA showed a water absorption capacity under 8.5%. The amount of components in a concrete mix, such as the mass of cement (C), fly ash (FA), water (W), superplasticizer (SP), fine natural aggregate (FNA), coarse natural or recycled aggregate (CNA, RCA) and their properties, such as: sand fineness modulus of sand (FM of FNA), water absorption capacity (WA), saturated surface dry density of the coarse aggregate mix (SSD), and maximum particle size of coarse aggregate(TM), are the main factors selected as input parameters used to construct the ANN models. The 28-day compressive strength was the only output value considered in this study. All the results were converted into equivalent 15 \(\times\) 30 cm cylindrical compressive strength [24]. Table 2. summarizes the properties of constituents used (detailed in Appendix A), their ranges, the amount of data in every one of them, mean and standard deviation. So, the set of data chosen for making the estimations is heterogeneous enough for the purposes of the proposed approach. The RILEM Technical Committee TC-242-MDC [17] also recommends considering all admixtures and reactive additives such as fly ash separately, to predict the creep, drying shrinkage and autogenous shrinkage of normal and high-strength concrete with multidecade applicability. This same technical committee highlighted the importance of the type of aggregate in the prediction of shrinkage and creep. Artificial Neural Networks have been shown to be useful to obtain good predictions in nonlinear processes and have been successfully used in several fields as control, for pattern recognition, learning, medical diagnosis, and a wide range of engineering applications, among others [40], and they present a special effectiveness when data are incomplete and noisy. In general, an ANN has three layers: and input layer, where input parameters are represented; several hidden layers, where data are processed; and an output layer, where output data are presented. Each one of the elements in the layers is called a neuron. The layers are interconnected by means of weights that must be modified along a training process, to obtain better fitting between the previously observed data and those generated in the output layer. The Artificial Neural Network used in this work has 11 neurons in the input layer and only 1 in the output layer, and four sets of trainings have been carried out combining 15 and 20 hidden layers (Figure 1.) with the training methods of Levenberg-Marquardt (LM) and Bayesian Regularization (BR). The first one, LM, is recommended for nonlinear optimization problems and it is supposed to display a better performance than the traditional Gauss-Newton Method. The second one, BR, gives a better generalization when data are difficult [41]. So, four training processes have been carried out to obtain the best possible fitting of the synaptic weights. For the training process of the ANN, data have been divided into three sets: a training set of 123 datapoints (70%), a validation set of 27 datapoints (15%), and a testing set of 27 datapoints (15%).