A Study of Size Effect in Shear Resistance of Reinforced Concrete Beams Based on Machine Learning

Abstract

Size effect is common in structural engineering where a large component fails at lower stress level than a tiny one made of the same material. As a result, the calculation of the loading capacity of a structural component need to be revised when size effect is considered, especially for the reinforced concrete (RC) beam under shear loading. However, due to inadequate theoretical analysis of the size effect in shear failure, the calculation of shear strength in codes all over the world is far from accurate. In this research, the machine learning method, which can efficiently analyse and process data, is applied to study the size effect in shear strength of beams via exploring the experimental data collected worldwide on shear failure of RC beams. The back propagation (BP) neural network model of shear strength of RC beams is built and trained by the data exist, and then give a prediction of shear strength. The values predicted are compared with the values calculated from codes and Bažant size effect formulas and the experimental data, which indicates the feasibility and superiority of machine learning method in dealing with size effect in shear failure.