Application of Artificial Neural Network (ANN) in the Determination of the Drillability Index (DI) of a Rock Mass

Abstract

Artificial Neural Networks (ANN) have been applied to many interesting problems in different areas of science, medicine and engineering and in some cases, they provide state of-the-art solutions. This paper investigates the application of an ANN model in mining to predict the Drillability Index (DI) of a rock mass given rock parameters such as uniaxial compressive strength, shear strength, tensile strength, abrasion and hardness. Drillability indicates whether penetration is easy or hard while penetration rate indicates whether it is fast or slow. Therefore, prediction of the drillability and penetration rate is very important in rock drilling. Penetration rate is a necessary value for the cost estimation and the planning of the drilling project. According to results of this study, Uniaxial Compressive Strength (UCS) rating has the highest weight of 0.051083 among the three parameters studied which reconfirms the literature review finding which indicates that UCS is the most important parameter in predicting drillability.