## 1 Introduction
The Levenberg-Marquardt backpropagation algorithm was used to train the network, and the best prediction model for the compressive strength of LWAC gave a determination coefficient of about 0.825 and a root mean square error (RMSE) of about 3.745. In [19], the behaviour of multiple linear regression (MLR) and regression tree (RT) models were compared with the ANN models proposed in [6], using the same dataset, and the authors showed that MLR models were not competitive in terms of explaining the compressive strength of LWAC, with a poor determination coefficient of about 0.77 and an RMSE of about 4.332. In addition, CHAID (Chi-squared automatic interaction detector) [20], exhaustive CHAID [21] and CRT (classification and regression trees) [22] were analysed. The best RT model was obtained with the CHAID algorithm, which yielded performance similar to that of the ANN models proposed in [6], with a determination coefficient of about 0.82 and an RMSE of about 3.808. In other studies, MLR and ANN models have also been compared in terms of predicting some of the properties of concrete. For example, in [23], ANNs and MLR were used to predict the compressive strength of concrete based on two predictor variables (the ultrasonic pulse velocity and the weight of concrete specimens), whereas in [24], categorised linear regression, MLR and ANN were employed to predict the compressive strength of structural LWAC. The results of both of these works indicated the higher accuracy of ANN models. In [25], MLR and ANN models were used to predict slump as well as 7-day and 28-day compressive strengths based on data obtained from plants in India using ready-mix concrete. The ANN models outperformed the MLR models, giving more accurate predictions for both slump and compressive strength. In [26], the use of ANNs to predict the compressive strength of recycled brick aggregate concrete was explored. The components of the concrete mixture formed the input data parameters (cement, water-cement ratio, crushed tile ratio, crushed brick ratio and natural aggregate ratio), and the results showedthat the type of clay aggregate used in the study (brick or tile) could be neglected. In [27], ANN, nonlinear regression (NLR) and RT models were compared to predict the 28-day compressive strength of recycled aggregate concrete. The ANN models outperformed both the NLR and RT models with nine predictor variables (cubic metre proportions of cement, natural fine aggregate, recycled fine aggregate, natural coarse aggregate--10 mm, natural coarse aggregate--20 mm, recycled aggregate--10 mm, recycled aggregate--20 mm, admixture and water). RT methods have also previously been applied to predict the elastic modulus of recycled aggregate concrete [28] and to address other problems in the area of civil engineering, such as the modelling of damage in reinforced concrete buildings [29]. In [30], a backpropagation ANN model was proposed to predict the bond strength for fibre-reinforced plastic (FRP) reinforced concrete at high temperatures. This ANN was tested by using 151 data points, and its precision was found to be slightly lower than that of the current mathematical models [31; 32; 33]. However, it had higher generality in predicting the bond strength when applied to inexpressible problems involving multiple influence parameters. In [34], two-level and hybrid ensembles of RT were used to predict the compressive strength of high-performance concrete and were found to outperform RT models. Recently, in [35], support vector regression (SVR), MLP, gradient boosting regressors (GBRs) and extreme GBR (or XGBoost) were also used to predict the compressive strength of high-performance concrete. The results were better than those reported in other studies [36; 37; 38; 39; 40], in which several machine learning techniques were proposed for the same problem. As reported in [34; 35], tree-based ensemble methods have strong potential in terms of predicting the compressive strength of high-performance concrete (see also [41]).