## 1 Introduction
In recent years, reliable models based on several Machine Learning (ML) techniques have been proposed in the literature to solve diverse engineering problems, as alternatives to other and more complex analytical or numerical models. These models are able to provide predictions through a self-learning process based on collected or existing databases. In parallel, during the last decades, a huge amount of data has been accumulated in the field of civil engineering due to the advance in experimental programs, monitoring, data acquisition, and processing. For these reasons, ML-based models have been successfully used in complex civil engineering problems, including geotechnical, materials and structural [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39], and some of them already focus on the torsional capacity of RC beams, including externally strengthened and combined RC beams [40; 41; 42; 43; 44; 45; 46; 47; 48]. The referred studies on the torsional capacity of RC beams are still very limited and they generally apply different ML techniques. This is due to the fact that several ML techniques have been proposed in the literature, which are able to be used to solve problems in the field of civil engineering, including for structural concrete [46]. Despite the successes achieved with the application of ML approaches in several previous studies, some key challenges still exist which prevent these models from being widely used to solve engineering problems. Perhaps the main key challenge is related with the selection process of the most appropriate and effective ML models to solve a particular civil engineering problem. For instance, some studies indicated that the best performances were reached with extreme gradient boosting (XGBoost) for problems involving the shear strength of RC beams [49; 50], whereas others have mentioned other ML models to be more performant for the same types of problems [41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55], namely Artificial Neural Networks (ANN). This includes the analysis of RC beams under torsion, where the shear effect is predominant [40; 41; 43; 44; 45; 46; 48]. Ensembles of Trees (ET) have also been used in such problems, although to a lesser extent and with limited success [44]. Explicit ML techniques, such as the M5P model tree [47], were also successfully used. The problem related with the selection process of the most appropriate effective ML model for RC beams under torsion needs more studies to compare the performance of different ML models. Particularizing for RC beams under torsion, another key challenge is related to the number of features to be considered as input variables for developing the ML models. The more selected number of input variables, the higher the prediction accuracy. However, the more complex the algorithm and the larger the computational cost. Hence, a compromise must be reached for practice. Decision Trees (DT) are among the most commonly used ML models in very different problems, and they show several distinct advantages. Many algorithms require data normalization before model building. Such variable transformations are not required with DT since the tree structure will remain the same despite the transformation. They also implicitly perform feature selection and are not sensitive to outliers. In addition, DT usually require less input variables when compared to other ML models, such as ANN, to achieve similar performances. For these reasons, DT were used as ML-based models for this study. This study aims to contribute to compare and find the best ML techniques to be applied to RC beams under pure torsion.