Abstract:The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength. Based on the test results, the regression analyses, support vector machine (SVM) and generalized regression neural network (GRNN) were used to find the relationship among rock cuttability, uniaxial confining stress applied to rock, uniaxial compressive strength (UCS) and tensile strength of rock material. It was found that the regression and SVM-based models can accurately reflect the variation law of rock cuttability, which presented decreases followed by increases with the increase in uniaxial confining stress and the negative correlation to UCS and tensile strength of rock material. Based on prediction models for revealing the optimal stress condition and determining the cutting parameters, the axial boom roadheader with many conical picks mounted was satisfactorily utilized to perform rock cutting in hard phosphate rock around pillar.

Key words: rock cuttability; rock indentation; prediction model; regression analysis; support vector machine; neural network