The advance rate(AR)of a tunnel boring machine(TBM)under hard rock conditions is a key parameter in the successful implementation of tunneling engineering.In this study,we improved the accuracy of prediction models by...The advance rate(AR)of a tunnel boring machine(TBM)under hard rock conditions is a key parameter in the successful implementation of tunneling engineering.In this study,we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting(XGBoost)with Bayesian optimization(BO)to model the TBM AR.To develop the proposed models,1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia.The database consists of rock mass and intact rock features,including rock mass rating,rock quality designation,weathered zone,uniaxial compressive strength,and Brazilian tensile strength.Machine specifications,including revolution per minute and thrust force,were considered to predict the TBM AR.The accuracies of the predictive models were examined using the root mean squares error(RMSE)and the coefficient of determination(R^(2))between the observed and predicted yield by employing a five-fold cross-validation procedure.Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model.The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R^(2) values of 0.0967 and 0.9806(for the testing phase),respectively.The results demonstrated the merits of the proposed BO-XGBoost model.In addition,variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.展开更多
VoLTE(Voice Over LTE)是架构在4G网络上全IP条件下的端到端语音解决方案,可以为客户提供更优的语音感知体验。笔者从单通VoLTE通话的各项网络指标出发,介绍了一种XGBoost机器学习的算法,论述了如何使用XGBoost算法建立VoLTE用户语音投...VoLTE(Voice Over LTE)是架构在4G网络上全IP条件下的端到端语音解决方案,可以为客户提供更优的语音感知体验。笔者从单通VoLTE通话的各项网络指标出发,介绍了一种XGBoost机器学习的算法,论述了如何使用XGBoost算法建立VoLTE用户语音投诉模型,对质差语音用户进行预测,指导一线人员优化提升用户通话感知,在用户投诉前发现并解决用户感知问题,提高用户满意度,进而避免用户流失。展开更多
基金funded by the National Science Foundation of China(41807259)the Innovation-Driven Project of Central South University(2020CX040),Chinathe Shenghua Lieying Program of Central South University,China(Principle Investigator:Dr.Jian Zhou).
文摘The advance rate(AR)of a tunnel boring machine(TBM)under hard rock conditions is a key parameter in the successful implementation of tunneling engineering.In this study,we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting(XGBoost)with Bayesian optimization(BO)to model the TBM AR.To develop the proposed models,1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia.The database consists of rock mass and intact rock features,including rock mass rating,rock quality designation,weathered zone,uniaxial compressive strength,and Brazilian tensile strength.Machine specifications,including revolution per minute and thrust force,were considered to predict the TBM AR.The accuracies of the predictive models were examined using the root mean squares error(RMSE)and the coefficient of determination(R^(2))between the observed and predicted yield by employing a five-fold cross-validation procedure.Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model.The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R^(2) values of 0.0967 and 0.9806(for the testing phase),respectively.The results demonstrated the merits of the proposed BO-XGBoost model.In addition,variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
文摘VoLTE(Voice Over LTE)是架构在4G网络上全IP条件下的端到端语音解决方案,可以为客户提供更优的语音感知体验。笔者从单通VoLTE通话的各项网络指标出发,介绍了一种XGBoost机器学习的算法,论述了如何使用XGBoost算法建立VoLTE用户语音投诉模型,对质差语音用户进行预测,指导一线人员优化提升用户通话感知,在用户投诉前发现并解决用户感知问题,提高用户满意度,进而避免用户流失。
