## 4 Comparison with the Results from Previous Studies  
When compared with the results from previous studies using ML models, referred in Section 1 and related mainly with RC beams under pure torsion, the following can be stated. The research article from [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) was the only one in which the authors applied two models based on ET, namely a Boosted and a Bagged model, to predict the torsional gain capacity of externally bonded FRP-RC beams, and not the full torsional capacity of common RC beams. They used additional inputs incorporating data about the strengthening system and a dataset with 157 beams. Since the predicted variable was different, only the \(R^{2}\) parameter is discussed here. With their models, the referred authors achieved values of 0.71 and 0.47 for the Boosted and Bagged models, respectively. These values are much lower than the ones achieved in this study. Reference [PERSON] and [PERSON] (2018) also used other models, namely four models based on a Gaussian Process Regression and five models based on Neural Networks. For all these models, the \(R^{2}\) ranged between 0.56 and 0.93, which still remains lower than the ones from this study. [PERSON] et al. [PERSON] et al. (2018) used the same database and the same inputs as in the present research, and applied a M5P model tree and a non-linear regression model to predict the torsional capacity of the RC beams. For the first one and for the testing set, they achieved the following metric performances: \(MAE=8.224\), \(RMSE=13.432\) and \(R^{2}=0.981\). When compared with the metric performances in Table 6 (also for the testing set), it can be stated that the models used in this study achieved similar results, although three of the models (BME, FRT and GTB) showed to be slightly better. [PERSON] et al. [PERSON] et al. (2018) used a Genetic-programming-based model to predict the torsional capacity of RC beams using a dataset with only 76 beams. The authors used 5 variables among 12 available as inputs. For the testing set, the following metric performances were achieved: \(RMSE=12.9\) and \(R^{2}=0.95\). Based on the results from Table 6 (also for the testing set), the same three previously referred models (BME, FRT and GTB) were shown to be slightly better in terms of \(RMSE\). Furthermore, all models showed \(R^{2}\) values higher than 0.95. Finally, Deifalla and Salem (2018); Deifalla and Salem (2018); Deifalla and Salem (2018) used different artificial neural network (ANN) or convolutional neural network (CNN) models to predict the torsional capacity of RC beams, with a much higher number of inputs (11 or 12) and different datasets (with 76 beams for Deifalla and Salem (2018); Deifalla and Salem (2018), 240 beams for Deifalla and Salem (2018) and 268 beams for Deifalla and Salem (2018)). Regarding the same metric performances used in this study, for reference Deifalla and Salem (2018) and for four different ANN models (BP and GA-BP neural networks, optimized BP and GA-BP neural networks), the metric performances ranged between the following values: \(MAE=6.742\) to \(11.548\), \(RMSE=10.154\) to \(17.758\) and \(R^{2}=0.846\) to \(0.950\). For reference Deifalla and Salem (2018), with only one ANN model (hybrid neural network: GA-MLP), the metric performances were as follows: \(MAE=6.94\) and \(R^{2}=0.980\). For reference Deifalla and Salem (2018) where eleven ANN algorithms were used, the author achieved the following range of values: \(R^{2}=0.9496\) to \(0.9876\). Finally, in Deifalla and Salem (2018), the authors proposed an improved bird swarm algorithm optimized 2D CNN, for which the following metric performance was achieved: \(MAE=2.9875\). When compared with the results from Table 6, it can be stated that the ET models in this study achieved very similar performance metrics as the ones from the ANN models, but with much lesser inputs.