This study is the result of long-term efforts of the authors’team to assess ground response of gob-side entry by roof cutting(GSERC)with hard main roof,aiming at scientific control for GSERC deformation.A comprehensi...This study is the result of long-term efforts of the authors’team to assess ground response of gob-side entry by roof cutting(GSERC)with hard main roof,aiming at scientific control for GSERC deformation.A comprehensive field measurement program was conducted to determine entry deformation,roof fracture zone,and anchor bolt(cable)loading.The results indicate that GSERC deformation presents asymmetric characteristics.The maximum convergence near roof cutting side is 458 mm during the primary use process and 1120 mm during the secondary reuse process.The entry deformation is closely associated with the primary development stage,primary use stage,and secondary reuse stage.The key block movement of roof cutting structure,a complex stress environment,and a mismatch in the supporting design scheme are the failure mechanism of GSERC.A controlling ideology for mining states,including regional and stage divisions,was proposed.Both dynamic and permanent support schemes have been implemented in the field.Engineering practice results indicate that the new support scheme can efficiently ensure long-term entry safety and could be a reliable approach for other engineering practices.展开更多
Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important f...Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important for pick design and rock cutting.This study proposed hybrid methods composed of boosting trees and Bayesian optimization(BO)for accurate evaluation of MCF.220 datasets including uniaxial compression strength,tensile strength,tip angle(θ),attack angle,and cutting depth,were collected.Four boosting trees were developed based on the database to predict MCF.BO optimized the hyper-parameters of these boosting trees.Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models.The hybrid model composed of BO and categorical boosting(BO-CatBoost)was the best.Its outstanding performance was attributed to its advantages in dealing with categorical features(θincluded 6 types of angles and could be considered as categorical features).A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF.Moreover,the influences of the input parameters on the model and their relationship with MCF were analyzed.Whenθincreased from 80°to 90°,it had a significant contribution to the increase of MCF.展开更多
基金Project(WPUKFJJ2019-19)supported by the Open Fund of State Key Laboratory of Water Resource Protection and Utilization in Coal Mining,ChinaProject(51974317)supported by the National Natural Science Foundation of China。
文摘This study is the result of long-term efforts of the authors’team to assess ground response of gob-side entry by roof cutting(GSERC)with hard main roof,aiming at scientific control for GSERC deformation.A comprehensive field measurement program was conducted to determine entry deformation,roof fracture zone,and anchor bolt(cable)loading.The results indicate that GSERC deformation presents asymmetric characteristics.The maximum convergence near roof cutting side is 458 mm during the primary use process and 1120 mm during the secondary reuse process.The entry deformation is closely associated with the primary development stage,primary use stage,and secondary reuse stage.The key block movement of roof cutting structure,a complex stress environment,and a mismatch in the supporting design scheme are the failure mechanism of GSERC.A controlling ideology for mining states,including regional and stage divisions,was proposed.Both dynamic and permanent support schemes have been implemented in the field.Engineering practice results indicate that the new support scheme can efficiently ensure long-term entry safety and could be a reliable approach for other engineering practices.
基金Project(52374153)supported by the National Natural Science Foundation of ChinaProject(2023zzts0726)supported by the Fundamental Research Funds for the Central Universities of Central South University,China。
文摘Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important for pick design and rock cutting.This study proposed hybrid methods composed of boosting trees and Bayesian optimization(BO)for accurate evaluation of MCF.220 datasets including uniaxial compression strength,tensile strength,tip angle(θ),attack angle,and cutting depth,were collected.Four boosting trees were developed based on the database to predict MCF.BO optimized the hyper-parameters of these boosting trees.Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models.The hybrid model composed of BO and categorical boosting(BO-CatBoost)was the best.Its outstanding performance was attributed to its advantages in dealing with categorical features(θincluded 6 types of angles and could be considered as categorical features).A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF.Moreover,the influences of the input parameters on the model and their relationship with MCF were analyzed.Whenθincreased from 80°to 90°,it had a significant contribution to the increase of MCF.
