Creep parameter inversion for high CFRDs based on improved BP neural network response surface method

The creep parameters of rockfill materials obtained from engineering analogy method or indoor tests often cannot accurately reflect the long-term deformation of high Concrete-Faced Rockfill Dams (CFRDs). This paper introduces an optimized inversion method based on multi-population genetic algorithm-...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2022-09, Vol.26 (18), p.9527-9541
Main Authors: Zhou, Xinjie, Sun, Xinjian, Li, Yongye, Zhang, Juntao, Li, Zhigang, Ma, Zhongxiang, Guan, Zhixuan, Zhang, Yuxiang, Zhang, Pengtao
Format: Article
Language:English
Subjects:
Application of Soft Computing
Artificial Intelligence
Computational Intelligence
Control
Engineering
Mathematical Logic and Foundations
Mechatronics
Robotics
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Summary:The creep parameters of rockfill materials obtained from engineering analogy method or indoor tests often cannot accurately reflect the long-term deformation of high Concrete-Faced Rockfill Dams (CFRDs). This paper introduces an optimized inversion method based on multi-population genetic algorithm-improved BP neural network and response surface method (MPGA-BPNN RSM). The parameters used for inversion are determined by parameter sensitivity analysis based on the statistical orthogonal test method. MPGA-BPNN RSM, validated by root-mean-square error, mean absolute percentage error, squared correlation coefficient ( R 2 ), etc., completely reflects the response between the creep parameters and the settlement calculation values obtained by finite element method (FEM). MPGA optimized the objective function to obtain the optimal creep parameters. The results show that the settlement values of Xujixia CFRD calculated by FEM using the inversion parameters has great consistency with the monitored values both in size and in distribution, suggesting that the model parameters obtained by the introduced creep parameter inversion method are feasible and effective. The introduced method can improve the inversion efficiency and the prediction accuracy in FEM applications.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-06735-3