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On-line Access: 2023-01-11

Received: 2022-06-04

Revision Accepted: 2022-08-31

Crosschecked: 2023-01-13

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Fei LV


Jia YU


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Journal of Zhejiang University SCIENCE A 2022 Vol.23 No.12 P.1027-1046


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

Author(s):  Fei LV, Jia YU, Jun ZHANG, Peng YU, Da-wei TONG, Bin-ping WU

Affiliation(s):  State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China

Corresponding email(s):   yujia@tju.edu.cn

Key Words:  Drilling efficiency, Prediction, Earth-rock excavation, Stacking-based ensemble learning, Improved cuckoo search optimization (ICSO) algorithm, Comprehensive effects of various factors, Hyper-parameter optimization

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Fei LV, Jia YU, Jun ZHANG, Peng YU, Da-wei TONG, Bin-ping WU. A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation[J]. Journal of Zhejiang University Science A, 2022, 23(12): 1027-1046.

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publisher="Zhejiang University Press & Springer",

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%A Fei LV
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%A Da-wei TONG
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200297

T1 - A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation
A1 - Fei LV
A1 - Jia YU
A1 - Jun ZHANG
A1 - Peng YU
A1 - Da-wei TONG
A1 - Bin-ping WU
J0 - Journal of Zhejiang University Science A
VL - 23
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2200297

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, eXtreme 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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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