Fracture toughness evaluation of ground granulated blast furnace slag concrete using experimental study and machine learning techniques

The relationship between mix design and fracture properties of ground granulated blast furnace slag (GGBFS) concrete is complex and influenced by many factors, such as mixture composition. This study evaluates the fracture toughness under mode-I, mode-II, and mixed-mode, performed on centrally straight-notched Brazilian discs. An experimental program generated an extensive database of the fracture toughness of GGBFS mortar, consisting of 143 specimens with different mix designs under different loading angle conditions. Then, a combination of support vector regression (SVR) algorithm and genetic algorithm (GA) as hyperparameter tuner was conducted to develop models for fracture toughness. The SVR algorithm was trained with Radial Basis Function (RBF), polynomial, sigmoid, and linear kernel functions. Based on the Taylor diagram, the models demonstrated robust performance in predicting fracture toughness. The performance metrics indicate that the RBF function is the most reliable model, with R2-score 0.95 and 0.94 for KIC and KIIC, respectively. The importance score of input features was determined with the permutation importance function. The angle between the crack and loading direction is the most influential parameter in the models.