Near Real-Time HDD Pullback Force Prediction Model Based on Improved Radial Basis Function Neural Networks
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 11, Issue 4
Abstract
Pipeline pullback is a crucial part of horizontal directional drilling (HDD) construction. Accurate pullback force prediction is the prerequisite for ensuring construction safety. However, owing to the influence of factors such as crossing length and formation conditions, it is difficult to predict the pullback force accurately using existing theories. In this paper, a hybrid model based on radial basis function neural networks (RBFNNs) is proposed, which can predict near real-time pullback force based on field monitoring data in the construction process. In this hybrid model, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to denoise the original data, which is conducive to better performance of the RBFNN model in prediction. To test the prediction accuracy of the proposed model, this paper takes two HDD projects in the Sichuan–East China Gas Project as examples. In addition, the stability of the prediction model and the effect of the sliding window length on the prediction results are discussed. The following conclusions can be drawn: (1) the proposed model has higher prediction accuracy than the empirical model, (2) the application of the denoising method can effectively improve prediction accuracy, and (3) the hybrid model has higher prediction stability than the original RBFNN.
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Data Availability Statement
All data generated or analyzed during the study are included in the published article.
Acknowledgments
This article was funded by China Scholarship Council (201708030006). Special thanks go to the NASSCO Jeffrey D. Ralston Memorial Scholarship from the National Association of Sewer Service Companies (NASSCO) and the Jack Doheny Memorial Training Scholarship from the National Association of Sewer Service Companies (NASSCO).
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 11 • Issue 4 • November 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Aug 13, 2019
Accepted: May 5, 2020
Published online: Jul 14, 2020
Published in print: Nov 1, 2020
Discussion open until: Dec 14, 2020
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