Abstract: 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.

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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