The prediction of penetration rate for percussive drills from indirect tests using artificial neural networks

pits, quarries, and on construction sites. The prediction of the penetration rate of drilling machines is very important for cost estimation and planning of rock excavation projects. Many researchers have investigated percussive drilling theoretically or experimentally and correlated the penetration rate with various rock properties. Protodyakonov (1962) developed drop tests and described the coefficient of rock strength (CRS) used as a measure of the resistance of rock to impact. The Protodyakonov test was subsequently modified by Paone, Madson, and Bruce (1969), Tandanand and Unger (1975), and Rabia and Brook (1980, 1981). Paone, Madson, and Bruce (1969) conducted research work on percussion drilling in the field. They concluded that uniaxial compressive strength (UCS), tensile strength, Shore hardness, and static Young’s modulus correlated tolerably well with penetration rates in nine hard and abrasive rocks. A much better correlation was obtained by using the CRS. Paone, Madson, and Bruce stated that no single property of a rock was completely satisfactory as a predictor of penetration rate. Tandanand and Unger (1975) developed an estimation equation that showed good correlation with actual penetration rates of percussive drills. They concluded that CRS was useful in predicting penetration rate and had a higher reliability than other rock properties. Rabia and Brook (1980, 1981) used a modified test apparatus to determine the rock impact hardness number and developed an empirical equation for predicting drilling rates for both down-thehole and drifter drills. The equation relating penetration rate to drill operating pressure, Shore hardness, and rock impact hardness number was found to give excellent correlation for field data obtained from down-the-hole and drifter drills. Selmer-Olsen and Blindheim (1970) conducted percussion drilling tests in the field using light drilling equipment with chisel bits. They established a good correlation between penetration rate and the drilling rate index (DRI) and found that rock hardness, strength, brittleness, and abrasivity were important in drilling. Selim and Bruce (1970) carried out percussive drilling experiments on nine rocks in the laboratory. They correlated the penetration rate for a specific drill rig with compressive strength, tensile strength, Shore hardness, apparent density, static and dynamic Young’s modulus, shear modulus, CRS, and percentage of quartz, and established linear predictive equations. They stated that the established equations could be used for predicting the performance of percussive drills. Schmidt (1972) correlated the penetration rate with compressive strength, tensile strength, Shore hardness, density, static and dynamic Young’s modulus, shear modulus, longitudinal velocity, shear velocity, and Poisson’s ratio. He found that only compressive strength and those properties highly correlated with it, such as tensile strength and Young’s modulus, The prediction of penetration rate for percussive drills from indirect tests using artificial neural networks