The main purpose of the present study was to provide a practical, convenient drillability prediction model based on rock mass characteristics, geological sampling from blast holes, and drill operational factors. Empir...The main purpose of the present study was to provide a practical, convenient drillability prediction model based on rock mass characteristics, geological sampling from blast holes, and drill operational factors. Empirical equations that predict drill penetration rate have been developed using statistical analyses of data from the Sarcheshmeh Copper Mine. Seven parameters of the rock or rock mass, including uniaxial compressive strength （UCS） of the rock, Schmidt hammer hardness value, quartz content, fragment size （dso）, alteration, and joint dip, are included in the model along with two operational parameters of the rotary drill, bit rotational speed and thrust. These parameters were used to predict values of the newly developed Specific Rock Mass Drillability （SRMD） index. Comparing measured SRMD values to those pre- dicted by the multi-parameter linear, or nonlinear, regression models showed good agreement. The cor- relation coefficients were 0.82 and 0.81. resoectively.展开更多
文摘The main purpose of the present study was to provide a practical, convenient drillability prediction model based on rock mass characteristics, geological sampling from blast holes, and drill operational factors. Empirical equations that predict drill penetration rate have been developed using statistical analyses of data from the Sarcheshmeh Copper Mine. Seven parameters of the rock or rock mass, including uniaxial compressive strength （UCS） of the rock, Schmidt hammer hardness value, quartz content, fragment size （dso）, alteration, and joint dip, are included in the model along with two operational parameters of the rotary drill, bit rotational speed and thrust. These parameters were used to predict values of the newly developed Specific Rock Mass Drillability （SRMD） index. Comparing measured SRMD values to those pre- dicted by the multi-parameter linear, or nonlinear, regression models showed good agreement. The cor- relation coefficients were 0.82 and 0.81. resoectively.
