Volume 16 Issue 4
Dec.  2023
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Article Navigation >  Water Science and Engineering  > 2023  >  16(4): 390-398
Yan-long Li, Qiao-gang Yin, Ye Zhang, Heng Zhou. 2023: Deformation prediction model of concrete face rockfill dams based on an improved random forest model. Water Science and Engineering, 16(4): 390-398. doi: 10.1016/j.wse.2023.09.005
Citation: Yan-long Li, Qiao-gang Yin, Ye Zhang, Heng Zhou. 2023: Deformation prediction model of concrete face rockfill dams based on an improved random forest model. Water Science and Engineering, 16(4): 390-398. doi: 10.1016/j.wse.2023.09.005
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Deformation prediction model of concrete face rockfill dams based on an improved random forest model

doi: 10.1016/j.wse.2023.09.005

  • a State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China;

  • b Power China Northwest Engineering Corporation Limited, Xi'an 710065, China

Funds:

This work was supported by the National Natural Science Foundation of China (Grant No. 51979224) and the China National Funds for Distinguished Young Scientists (Grant No. 52125904).

  • Received Date: 2022-11-20
  • Accepted Date: 2023-08-24
    • Available Online: 2023-12-14
    Abstract
  • The unique structure and complex deformation characteristics of concrete face rockfill dams (CFRDs) create safety monitoring challenges. This study developed an improved random forest (IRF) model for dam health monitoring modeling by replacing the decision tree in the random forest (RF) model with a novel M5' model tree algorithm. The factors affecting dam deformation were preliminarily selected using the statistical model, and the grey relational degree theory was utilized to reduce the dimensions of model input variables. Finally, a deformation prediction model of CFRDs was established using the IRF model. The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm. The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models. At point ES-10, the performance evaluation indices of the IRF model were superior to those of the M5' model tree and RF models and the classical support vector regression (SVR) and back propagation (BP) neural network models, indicating the satisfactory performance of the IRF model. The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10, demonstrating its strong anti-interference and generalization capabilities. This study has developed a novel method for forecasting and analyzing dam settlements with practical significance. Moreover, the established IRF model can also provide guidance for modeling health monitoring of other structures.

     

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