TY - JOUR
TI - Deformation prediction method of soft rock in deep shaft by machine learning
AU - Wang Guo-Yuan
AU - Geng Qian-Cheng
AU - You Shuang
AU - Fan Wen-Bo
AU - Feng Qi-Xing
JN - Thermal Science
PY - 2025
VL - 29
IS - 2
SP - 1395
EP - 1401
PT - Article
AB - Predicting the unsupported deformation behavior of a shaft is crucial for  evaluating the stability of the rock mass, selecting an appropriate support  scheme. Random forest, XGBoost, LightGBM, and K-nearest neighbors  regression models were trained for database, and their accuracy was  evaluated. It aimed to examine the effects of various parameters on shaft  deformation, including the maximum tangential stress of the surrounding  rock, elastic modulus, Poisson's ratio, cohesion, internal friction angle,  and rock mass compressive strength. The results indicate that the  coefficient of determination for random forest model is outperformed the  other models. The importance of the characteristic parameters, in order, is  cohesion, rock mass compressive strength, elastic modulus, rock compressive  strength, internal friction angle, Poisson's ratio, and maximum tangential  stress of the surrounding rock.