Volume 16 Issue 4
Dec.  2023
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Article Navigation >  Water Science and Engineering  > 2023  >  16(4): 419-428
Qin Ke, Ming-chao Li, Qiu-bing Ren, Wen-chao Zhao. 2023: Rockfill material uncertainty inversion analysis of concrete-faced rockfill dams using stacking ensemble strategy and Jaya optimizer. Water Science and Engineering, 16(4): 419-428. doi: 10.1016/j.wse.2023.09.001
Citation: Qin Ke, Ming-chao Li, Qiu-bing Ren, Wen-chao Zhao. 2023: Rockfill material uncertainty inversion analysis of concrete-faced rockfill dams using stacking ensemble strategy and Jaya optimizer. Water Science and Engineering, 16(4): 419-428. doi: 10.1016/j.wse.2023.09.001
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Rockfill material uncertainty inversion analysis of concrete-faced rockfill dams using stacking ensemble strategy and Jaya optimizer

doi: 10.1016/j.wse.2023.09.001

  • a State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China;

  • b Bei Fang Investigation, Design & Research Co., Ltd, Tianjin 300222, China

Funds:

This work was supported by the National Natural Science Foundation of China (Grants No. 51879185 and 52179139) and the Open Fund of the Hubei Key Laboratory of Construction and Management in Hydropower Engineering (Grant No. 2020KSD06).

  • Received Date: 2022-12-20
  • Accepted Date: 2023-08-24
    • Available Online: 2023-12-14
    Abstract
  • Numerical simulation of concrete-faced rockfill dams (CFRDs) considering the spatial variability of rockfill has become a popular research topic in recent years. In order to determine uncertain rockfill properties efficiently and reliably, this study developed an uncertainty inversion analysis method for rockfill material parameters using the stacking ensemble strategy and Jaya optimizer. The comprehensive implementation process of the proposed model was described with an illustrative CFRD example. First, the surrogate model method using the stacking ensemble algorithm was used to conduct the Monte Carlo stochastic finite element calculations with reduced computational cost and improved accuracy. Afterwards, the Jaya algorithm was used to inversely calculate the combination of the coefficient of variation of rockfill material parameters. This optimizer obtained higher accuracy and more significant uncertainty reduction than traditional optimizers. Overall, the developed model effectively identified the random parameters of rockfill materials. This study provided scientific references for uncertainty analysis of CFRDs. In addition, the proposed method can be applied to other similar engineering structures.

     

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