Torsional Capacity Prediction of Reinforced Concrete Beams Using Machine Learning Techniques Based on Ensembles of Trees

For the design or assessment of framed concrete structures under high eccentric loadings, the accurate prediction of the torsional capacity of reinforced concrete (RC) beams can be critical. Unfortunately, traditional semi-empirical equations still fail to accurately estimate the torsional capacity of RC beams, namely for over-reinforced and high-strength RC beams. This drawback can be solved by developing accurate Machine Learning (ML) based models as an alternative to other more complex and computationally demanding models. This goal has been herein addressed by employing several ML techniques and by validating their predictions. The novelty of the present article lies in the successful implementation of ML methods based on Ensembles of Trees (ET) for the prediction of the torsional capacity of RC beams. A dataset incorporating 202 reference RC beams with varying design attributes was divided into testing and training sets. Only three input features were considered, namely the concrete area (area enclosed within the outer perimeter of the cross-section), the concrete compressive strength and the reinforcement factor (which accounts for the ratio between the yielding forces of both the longitudinal and transverse reinforcements). The predictions from the used models were statistically compared to the experimental data to evaluate their performances. The results showed that ET reach higher accuracies than a simple Decision Tree (DT). In particular, The Bagging Meta-Estimator (BME), the Forests of Randomized Trees (FRT), the AdaBoost (AB) and the Gradient Tree Boosting (GTB) reached good performances. For instance, they reached values of R-2 (coefficient of determination) in the range between 0.982 and 0.990, and values of cvRMSE (coefficient of variation of the root mean squared error) in the range between 10.04% and 13.92%. From the obtained results, it is shown that these ML techniques provide a high capability for the prediction of the torsional capacity of RC beams, at the same level of other more complicated ML techniques and with much fewer input features.