Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting out of Isfahan University of Technolog y by NewsRx editors, research stated, “Estimating penetration rates of Jumbo dri lls is crucial for optimizing underground mining drilling processes, aiming to r educe costs and time.” Our news editors obtained a quote from the research from Isfahan University of T echnology: “This study investigates various regression and machine learning meth ods, including Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forests (RF), to predict the penetration rates (ROP) using multivariate inputs such as operation parameters and rock mass characteristics. The Rock Mass Drillability Index (RDi), incorporating both intact rock properties and structu ral parameters, was utilized to characterize the rock mass. The dataset was spli t into 80% for training and 20% for testing. Perform ance metrics including correlation coefficient (R2), variance accounted for (VAF ), mean absolute error (MAE), mean absolute percentage error (MAPE), and root me an square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. SVR exhibited the best prediction performance for ROP, achie ving the highest R2, lowest RMSE, MAE, and MAPE, as well as the largest VAF valu es of 0.94, 0.15, 0.11, 4.84, and 94.13 during training, and 0.91, 0.19, 0.13, 6 .02, and 91.11 during testing, respectively.”
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