A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation  

作　　者：Fei LV Jia YU Jun ZHANG Peng YU Da-wei TONG Bin-ping WU 

机构地区：[1]State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300350,China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2022年第12期1027-1046,共20页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：supported by the Yalong River Joint Funds of the National Natural Science Foundation of China(No.U1965207);the National Natural Science Foundation of China(Nos.51839007,51779169,and 52009090)。

摘　　要：Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.

关 键 词：Drilling efficiency PREDICTION Earth-rock excavation Stacking-based ensemble learning Improved cuckoo search optimization(ICSO)algorithm Comprehensive effects of various factors Hyper-parameter optimization 

分 类 号：TU751[建筑科学—建筑技术科学] TP18[自动化与计算机技术—控制理论与控制工程]