Document Type : Original Research Paper
Author
Department of Mining Engineering, Bandung Institute of Technology, Kota Bandung, Indonesia
Abstract
Drillability is one of the significant issues in rock engineering. The drilling rate index (DRI) is an important tool in analyzing the drillability of rocks. Several efforts have been made by the researchers to correlate and evaluate DRI of rocks. The ensemble learning methods including the decision tree (DT), adaptive boosting (AdaBoost), and random forest (RF) are employed in this research work in order to predict DRI of rocks. A drillability database with four parameters is compiled in this work. A relationship between the input parameters and DRI is established using the simple regression analysis. In order to train the model, different mechanical properties of rocks incorporating the uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), brittleness test (S20), and sievers’ J-miniature drill value (Sj) are taken as the input variables. The original DRI database is randomly divided into the training and test sets with an 80/20 sampling method. Various algorithms are developed, and consequently, several approaches are followed in order to predict DRI of the rock samples. The model performance has revealed that RF predicts DRI with a high accuracy rate. Besides, the Monte Carlo simulations exhibit that this approach is more reliable in predicting the probability distribution of DRI. Therefore, the proposed model can be practiced for the stability risk management and the investigative design of DRI.
Keywords
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