Water Science and Engineering 2019, 12(3) 196-204 DOI:   https://doi.org/10.1016/j.wse.2019.09.004  ISSN: 1674-2370 CN: 32-1785/TV

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Keywords
Shuibuya rockfill dam
Parameter back analysis
Response surface method
Duncan EB model
Time-dependent deformation
Authors
PubMed

Long-term deformation analysis of Shuibuya concrete face rockfill dam (China) based on response surface method and improved genetic algorithm

Fu-hai Yao a, Shao-heng Guan a, He Yang b,*, Yuan Chen a, Huan-feng Qiu c, Gang Ma a, Qi-wen Liu c

a School of Water Resources and Hydropower Engineering, Wuhan University,Wuhan 430072, China
b School of Civil Engineering and Architecture, Wuhan University, Wuhan 430070, China
c Guiyang Engineering Corporation Limited, Guiyang 550081, China

Abstract

Due to the size effects of rockfill materials, the settlement difference between numerical simulation and in situ monitoring of rockfill dams is a topic of general concern. The constitutive model parameters obtained from laboratory triaxial tests often underestimate the deformation of high rockfill dams. Therefore, constitutive model parameters obtained by back analysiswere used to calculate and predict the long-term deformation of rockfill dams. Instead of using artificial neural networks (ANNs), the response surface method (RSM) was employed to replace the finite element simulation used in the optimization iteration. Only 27 training samples were required for RSM, improving computational efficiency compared with ANN, which required 300 training samples. RSM can be used to describe the relationship between the constitutive model parameters and dam settlements. The inversion results of the Shuibuya concrete face rockfill dam (CFRD) show that the calculated settlements agree with the measured data, indicating the accuracy and efficiency of RSM.

Keywords Shuibuya rockfill dam   Parameter back analysis   Response surface method   Duncan EB model   Time-dependent deformation  
Received 2019-01-10 Revised 2019-05-12 Online: 2019-09-30 
DOI: https://doi.org/10.1016/j.wse.2019.09.004
Fund:

his work was supported by the National Natural Science Foundation of China (Grant No. 51579193) and the Science and Technology Planning Project of Guizhou Province (Grant No. [2016]1154).

Corresponding Authors: He Yang
Email: 1019463985@qq.com
About author:

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