Drillability classification and identification for rock mass based on machine learning

Abstract

In order to better apply the drilling method to underground mines, rock drillability classification and identification in situ by drilling process monitoring technology is a convenient and effective method to achieve the rock mass drillability. In this study, a database was established based on 188 groups of drilling parameters, drillability parameters and rock mechanics parameters. By analyzing the correlation between mechanical parameters and drillability parameters, rock drillability was classified using the TOPSIS-RMR method. Then, drilling force (F), torque (T), rotation speed (N), rate of penetration (V), specific energy (SE) and drillability index (Id) were used as machine learning input variables to predict drillability grades. Finally, the machine learning classification models include SVM, ELM, BPNN, RBF, RF and LSTM are compared to select the optimal model. The efforts and results can be used to evaluate the rock mass drillability and provide support for the design optimization of drilling and blasting method. It can effectively protect the safety and improve efficiency of underground mining.