[1] Zhang Fan, Hu Wusheng,. Application of neural network merging modelin dam deformation analysis [J]. Journal of Southeast University (English Edition), 2013, 29 (4): 441-444. [doi:10.3969/j.issn.1003-7985.2013.04.016]

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Application of neural network merging modelin dam deformation analysis()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 4

Page:

441-444

Research Field:

Other Disciplines

Publishing date:

2013-12-20

Info

Title:

Application of neural network merging modelin dam deformation analysis

Author(s):

Zhang Fan;  Hu Wusheng

School of Transportation, Southeast University, Nanjing 210096, China

Keywords:

dam deformation analysis;  neural network;  statistical model;  merging model

PACS:

TV698.1

DOI:

10.3969/j.issn.1003-7985.2013.04.016

Abstract:

In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation(BP)neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ±0.477 and ±0.390 mm, respectively, while the prediction accuracy of the merging model is ±0.318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability.

References:

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Memo

Memo:

Biographies: Zhang Fan(1987—), male, graduate; Hu Wusheng(corresponding author), male, doctor, professor, wusheng.hu@163.com.
Foundation item: The Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXLX11_0143).
Citation: Zhang Fan, Hu Wusheng. Application of neural network merging model in dam deformation analysis[J].Journal of Southeast University(English Edition), 2013, 29(4):441-444.[doi:10.3969/j.issn.1003-7985.2013.04.016]

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