Abstract
A full recognition of the role of rock drillability plays a significant role in drilling parameters optimization, bit selection and prediction of penetration rate in rock drilling projects. However, the traditional forecasting methods, such as empirical formulae, are inaccurate because most of them do not take into consideration all the relevant factors. In this paper, new intelligence method, namely relevance vector regression (RVR) optimized by artificial bee colony (ABC) algorithm is introduced to forecast the unseen data. In this model, diametral point load strength index (Is(50) →), uniaxial compressive strength, shore scleroscope hardness, Brazilian tensile strength and axial point load strength index (Is(50)↓)) were utilized as the input parameters, while the rock drillability was the output parameter. The performances of the proposed predictive model was examined according to two performance indices, i.e., coefficient of determination (R2) and mean square error (MSE). The obtained results of this study indicated that the RVR-ABC model is a reliable method to forecast rock drillability with a higher degree of accuracy (R2 = 0.9363 and MSE = 0.01354).
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