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Hybrid machine learning for pullback force forecasting during horizontal directional drilling
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DOI：10.1016/j.autcon.2021.103810.png)
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Abstract
En 中文
This paper presents a hybrid machine learning model for predicting the pullback force in horizontal directional drilling (HDD) construction. The model combines the nondominated sorting genetic algorithm II (NSGA-II) and support vector machine (SVM). NSGA-II is used to optimize two hyperparameters in SVM. Different from other optimization algorithms, NSGA-II is a multi-objective optimizer, which considers prediction accuracy and stability. The proposed model is applied to two practical HDD projects in China. The prediction result is compared with the actual monitoring data, which shows that the mean absolute percentage errors (MAPE) are less than 7%. The primary conclusions are as follows: (1) The proposed model's accuracy and stability are better than those of the two benchmark models; (2) Machine learning models can predict the pullback force more accurately than the empirical model in the construction phase, and the maximum MAPE does not exceed 17%; (3) The running time of the proposed model is short, and it is feasible in practical application. Moreover, this paper discusses the practical application of machine learning models in HDD construction and the future development direction.
Keywords:
Horizontal directional drilling
Pullback force
Forecasting
Machine learning
Support vector machine
Multi-objective optimizer
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