A data-driven approach to predicting multifactor-influenced flexural size effect and fracture behaviors of concrete

Abstract

Flexural size effect, originating from the fracture characteristics of materials, is a common phenomenon in concrete. Conventionally, time-consuming and labor-intensive experiments are required to investigate the flexural size effect and fracture behaviors of concrete. To tackle the limitations, a data-driven approach was adopted to predict the multifactor-influenced flexural size effect and fracture behaviors of concrete by gene expression programming (GEP) due to its capability of addressing non-linear problems and developing empirical equations with multiple input variables. Results show that the GEP models can accurately predict nominal flexural strength (R2, 0.890) and fracture toughness (R2, 0.946). Parametric analysis reveals that the compressive strength and tensile strain capacity positively impact the nominal flexural strength and fracture toughness of concrete. Based on the GEP model, a multifactor-influenced size effect law (SEL) is proposed to predict the nominal flexural strength by incorporating both material and geometric parameters, removing the need for extensive experimental investigations. The findings provide generalized models to predict the nominal flexural strength and fracture toughness of various materials at different sizes.