Abstract
目的
对钻进效率进行精确预测是制定土方开挖进度计划的关键。但现有预测方法多采用单个机器学**均绝对百分比误差(MAPE)分别提高了16.43%和4.88%。
Abstract
Accurate prediction of drilling efficiency is critical for develo** 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.
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Acknowledgments
This work is supported by the Yalong River Joint Funds of the National Natural Science Foundation of China (No. U1965207) and the National Natural Science Foundation of China (Nos. 51839007, 51779169, and 52009090).
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Jia YU designed the research. Fei LV processed the corresponding data. Fei LV and Jun ZHANG wrote the first draft of the manuscript. Peng YU and Da-wei TONG helped to organize the manuscript. Jia YU and Bin-** WU revised and edited the final version.
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Fei LV, Jia YU, Jun ZHANG, Peng YU, Da-wei TONG, and Bin-** WU declare that they have no conflict of interest.
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Lv, F., Yu, J., Zhang, J. et al. A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation. J. Zhejiang Univ. Sci. A 23, 1027–1046 (2022). https://doi.org/10.1631/jzus.A2200297
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DOI: https://doi.org/10.1631/jzus.A2200297
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