The Prediction of Penetration Rate For Percussive Drills From Indirect Tests Using Artificial Neural Networks

Abstract

Percussive drills are widely used in engineering projects such as mining and construction. The prediction of penetration rates of drills by indirect methods is particularly useful for feasibility studies. In this investigation, the predictability of penetration rate for percussive drills from indirect tests such as Shore hardness, P-wave velocity, density, and quartz content was investigated using firstly multiple regression analysis, then by artificial neural networks (ANNs). Operational pressure and feed pressure were also used in the analyses as independent variables. ANN analysis produced very good models for the prediction of penetration rate. The comparison of ANN models with the regression models indicates that ANN models are the more reliable. It is concluded that penetration rate for percussive drills can be reliably estimated from the Shore hardness and density using ANN analysis.