Prediction of Mode-I rock fracture toughness using support vector regression with metaheuristic optimization algorithms

In this work, the support vector regression method is combined with six metaheuristic optimization models of particle swarm optimization, grey wolf optimization, multiverse optimization, moth flame optimization, sine cosine algorithm, and social spider optimization to predict Mode-I rock fracture toughness. In addition, four other models of random regression forest, extra regression tree, decision regression tree, and fully-connected neural network that previously were used to predict the Mode-I rock fracture toughness by other researchers, was applied by this study. 250 datasets, including six input parameters and one output parameter (Mode-I rock fracture toughness) were utilized in the models obtained through the cracked Chevron notched Brazilian disc testing specimens suggested by the ISRM in the laboratory. Finally, the hybrid model of support vector regression-particle swarm optimization produced the most accurate results and it was recommended to predict the Mode-I rock fracture toughness. Also, the mutual information test was used to examine the impact of each input parameter on the Mode-I rock fracture toughness. Finally, the uniaxial tensile strength was identified as the most effective parameter on the Mode-I rock fracture toughness.