Optimizing fracture parameters in order to select based on theoretical concepts and concrete fracture energy prediction

Abstract

Fracture energy is a critical factor in modeling the fracture behavior of quasi-brittle materials like concrete, a long-standing interest in the safety design of structures. Concrete exhibits sophisticated non-linear behavior during fracture and crack growth. The prediction accuracy of fracture energy using existing equations has always been of extensive interest to researchers. The current study has optimized the coefficients of a series of well-known approximate models using the whale optimization algorithm. This optimization allows for more accurate predictions of concrete's fracture energy, taking into account fracture parameters such as compressive strength, maximum aggregate size, and water-to-cement ratio. Compressive strength, maximum aggregate size, water-to-cement ratio, and aggregate type (river or crushed) were extracted and tabulated from reliable literature, forming a comprehensive database. An approximate average prediction formula was compiled from this data. Using 70% of the specimens from this database (374 samples) and the whale algorithm, the constant coefficients of models from previous studies were optimized. This optimization minimizes the difference between the laboratory-measured fracture energy and the model predictions. As a result, these optimized models offer a more reliable tool for estimating the fracture mechanism in concrete structures.