The Slope Deformation Forecast Model Based on Kalman Filter and Wavelet Neural Network

Bing Jun Shi; Yong Fen Ruan; Qi Li; Yong Hong Wu

doi:10.4028/www.scientific.net/AMR.671-674.323

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 671-674The Slope Deformation Forecast Model Based on...

The Slope Deformation Forecast Model Based on Kalman Filter and Wavelet Neural Network

Abstract:

Deformation is the macroscopic index for the structure of geotechnical engineering, it is important for the design and construction of geotechnical engineering to monitor the deformation and analyze the monitored data. Kalman filter can enhance the effectiveness of the monitored data and wavelet neural network has the favorable time－frequency localization features and self－learning function. Firstly, the monitored data has been filtered by Kalman filter, and then a deformation forecast model will be established by means of combining with neural network wavelet to predict the deformation of actual engineering. The result shows that the forecast model is successful and effective to forecast the slope deformation.

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Periodical:

Advanced Materials Research (Volumes 671-674)

Pages:

323-327

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.671-674.323

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Online since:

March 2013

Authors:

Bing Jun Shi*, Yong Fen Ruan, Qi Li, Yong Hong Wu

Keywords:

Deformation Forecast, Kalman Filter (KF), Slope Engineering, Wavelet Neural Network (WNN)

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* - Corresponding Author

References

[1] Runqiu Huang, Qiang Xu. The analysis principle and application in geology generalized system [M]. Beijing: Geological Press , 1997. (In Chinese).

Google Scholar

[2] Xiufeng He. The new method and application of deformation monitor [M]. Beijing: Science Press, 2007. (In Chinese).

Google Scholar

[3] Hao Chen. Research on Short-term Traffic Flow Forecasting Based on Kalman Filter and Wavelet Neural Network[D]. The master thesis of Lanzhou Jiaotong University, 2007. (In Chinese).

Google Scholar

[4] Xizhang Cui, Zongchou Yu, Benzao Tao. Generalized survey adjustment[M]. Beijing: Surveying and Mapping Press, 2001. (In Chinese).

Google Scholar

[5] Xiaoyang Zheng. Wavelet neural network and its application[D]. The master thesis of Chongqing University. 2003. (In Chinese).

Google Scholar

[6] Yuansong Li, Xinping Li, Yifei Dai. The forecasting application of Wavelet neural network in high steep slope displacement [J]. Journal of Wuhan university of Science and Technology, 2010, 32 (9). (In Chinese).

Google Scholar