Machine learning as alternative strategy for the numerical prediction of the shear response in reinforced and prestressed concrete beams

Abstract

Some materials, such as reinforced and prestressed concrete, involve non-linear constitutive relationships in elasticity problems defined on them. In particular, the shear strength of a reinforced concrete beam may be calculated by considering a diagonal struts field in the context of the so-called “Compression Field Theories” (CFTs). This work presents an efficient Machine Learning method alternative to numerical methods for obtaining the full shear response of reinforced and prestressed concrete beams based on CFT regarding stresses, strains, and crack angles. For that, a regression task is developed using state-of-the-art Machine Learning (ML) models. A ML model per output variable is trained with the existing Newton-Raphson solutions database. The solvability region of the embedded steel constitutive model is also considered, demonstrating the comprehensive character of the proposed method. The model is validated on two real beam responses, where the results obtained demonstrate that this alternative method based on ML algorithms effectively addresses the problem of prediction of the shear response in reinforced and prestressed concrete beams. The proposed ML approximation performs reasonably well without requiring any initial approximations. Moreover, the ML regressor here developed shows a low dependence on the concrete tension stiffening area surrounding the steel reinforcement, which significantly improves the performance of this methodology in a higher number of design cases. Thus, in the practical context of structural engineering, this last approach establishes an efficient alternative procedure for obtaining numerical solutions of mechanical models based on the CFTs framework.