Development of Prediction Models for the Torsion Capacity of Reinforced Concrete Beams Using M5P and Nonlinear Regression Models

Abstract

Torsional strength is related with one of the most critical failure types for the design and assessment of reinforced concrete (RC) members due to the complexity of the associated stress state and low ductility. Previous studies have shown that reliable methods to predict the torsional strength of RC beams are still needed, namely for over-reinforced and high-strength RC beams. This research aims to offer a novel set of models to predict the torsional strength of RC beams with a wide range of design attributes and geometries by using advanced M5P tree and nonlinear regression models. For this, a broad database with 202 experimental tests is used to generate highly reliable and resilient models. To build the models, three independent variables related with the properties of the RC beams are considered: concrete cross-section area (area enclosed within the outer perimeter of the cross-section), concrete compressive strength, and torsional reinforcement factor (which accounts for the type—longitudinal or transverse—amount, and yielding strength of the torsional reinforcement). In contrast to multiple nonlinear regression approaches, the findings show that the M5P tree approach has the best estimation in terms of both accuracy and safety. Furthermore, M5P model predictions are far more accurate and safer than the most prevalent design equations. Finally, sensitivity and parametric studies are used to confirm the robustness of the presented models.