Abstract
Current study results on Machine Learning have been published. According to news reporting originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “Rapid acquisition of rock mechanical parameters and accurate identification of rock drillability are important to guide the safe construction of different scale drilling engineering (wells and boreholes) and high-efficient excavation of rock engineering. A database is established based on 281 sets of drilling parameters and rock mechanical parameters collected from four micro drilling experiments.” Funders for this research include National Natural Science Foundation of China (NSFC), Science and Technology Innovation Program of Hunan Province, China, Fundamental Research Funds for the Central Universities. Our news editors obtained a quote from the research from the School of Resources & Safety Engineering, “The drilling parameters in the database include drilling force (F), torque (T), rotational speed (N), and rate of penetration (Ⅴ), from which the specific energy (SE) and the drillability index (I-d) are calculated. With these parameters as input, fitting regression analysis and machine learning regression are used to predict the uniaxial compressive strength (UCS) of rocks. Furthermore, TOPSIS-RSR method is used to achieve rock drillability classification, and machine learning classification methods are used to perceive and identify drillability. In the prediction and recognition process, the accuracies of different methods are compared to determine the optimal model.”
NETL
NSTL
Journal Of Central South Univ