Machine learning methods for predicting the uniaxial compressive strength of the rocks: a comparative study

Abstract

The uniaxial compressive strength (UCS) of rocks is a critical index for evaluating the mechanical properties and construction of an engineering rock mass classification system. The most commonly used method for determining the UCS in laboratory settings is expensive and time-consuming. For this reason, UCS can be estimated using an indirect determination method based on several simple laboratory tests, including point-load strength, rock density, longitudinal wave velocity, Brazilian tensile strength, Schmidt hardness, and shore hardness. In this study, six data sets of indices for different rock types were utilized to predict the UCS using three nonlinear combination models, namely back propagation (BP), particle swarm optimization (PSO), and least squares support vector machine (LSSVM). Moreover, the best prediction model was examined and selected based on four performance prediction indices. The results reveal that the PSO–LSSVM model was more successful than the other two models due to its higher performance capacity. The ratios of the predicted UCS to the measured UCS for the six data sets were 0.954, 0.982, 0.9911, 0.9956, 0.9995, and 0.993, respectively. The results were more reasonable when the predicted ratio was close to a value of approximately 1.