Estimating Uniaxial Compressive Strength of Sedimentary Rocks with Leeb Hardness Using Support Vector Machine Regression Analysis and Artificial Neural Networks

Uniaxial compressive strength (UCS) of rock materials is a rock property that should be determined for the design and stability of structures before underground and aboveground engineering projects. However, it is impossible to determine the properties of rocks such as UCS directly due to the lack of standardized sample preparation, necessary equipment, etc. In this case, the UCS of rocks is predicted by index test methods such as hardness, ultrasound velocity, etc. Determining the hardness of rocks is relatively more practical, fast, and inexpensive than other properties. In this study, the UCS of sedimentary rocks was predicted as a function of Leeb hardness using artificial neural network (ANN) and Support Vector Machine (SVM) regression analysis. With the proposed ANN and SVM regression models, it is aimed to obtain more accurate and faster prediction values. To better train the models created in the study, the number of data was increased by compiling data from the studies in the literature. The UCS values predicted by the models obtained with two different methods and the measured UCS values were statistically compared. It was proved that the models created with ANN and SVM regression can be used reliably in predicting UCS values..