Paper Info
Title
Creep parameter inversion for high CFRDs based on improved BP neural network response surface method
Abstract
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 (R2), 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.
Year
DOI
Venue
2022
10.1007/s00500-022-06735-3
Soft Computing
Keywords
DocType
Volume
Parameter inversion, Sensitivity analysis, MPGA-BPNN RSM, Creep, High CFRDs
Journal
26
Issue
ISSN
Citations 
18
1432-7643
0
PageRank 
References 
Authors
0.34
2
9
Name
Order
Citations
PageRank
Xinjie Zhou100.34
Xinjian Sun200.34
Yongye Li300.34
Juntao Zhang400.34
Zhigang Li53411.35
Zhongxiang Ma600.34
Zhixuan Guan700.34
Yuxiang Zhang81115.58
Pengtao Zhang900.34