An efficient experimental model to estimate the performance of the raise borer drilling machine using linear and nonlinear regression approaches in the Azad Dam in Iran

This research evaluates the chief shaft in the Azad Dam hydroelectric power plant in Iran using a raise borer machine (RBM). Core samples were taken from the exploratory boreholes of the 210-m-long pressurized shaft. This study aims to determine new experimental models using linear and nonlinear regression approaches to estimate the operating parameters of RBM reaming, including daily progress rate, torque, thrust force, instantaneous penetration rate, power consumption, and the field’s specific energy. Data on geological and operating parameters were sampled and recorded from two separate phases during field studies of the main shaft drilling. The results of field studies in secondary reaming, geological mapping, and laboratory results were used statistically. Statistical modeling showed that the rock quality index of RBM (Q_r) has the highest correlation coefficient in linear relationships with the operating parameters. Rock quality designation (RQD) and Q_r are the most significant parameters affecting the RBM’s rotational speed and torque estimate. Analyzing the development of nonlinear models showed that RQD performs well in estimating RBM’s rotational speed and torque. Also, the field penetration index was a great measure to estimate the drilling’s specific energy. The correlation coefficient in predicting the operating parameters of the RBM’s penetration rate, rotational speed, and torque equaled 0.84, 0.71, and 0.7, respectively. Also, the RMSE value based on nonlinear regression for all three parameters equaled 0.24%, 1.35%, and 3.1%, respectively, indicating a low error in estimating RBM performance.