Bibliographic Details
| Title: |
A new prediction method for surface settlement of deep foundation pit in pelagic division based on Elman-Markov model. |
| Authors: |
Zhan, Yubao, Zhang, Jun, Liu, Qihua, Zheng, Pengqiang |
| Source: |
Arabian Journal of Geosciences; Jul2021, Vol. 14 Issue 14, p1-9, 9p |
| Abstract: |
Elman neural network is a kind of typical dynamic recurrent neural network. It can learn not only the spatial pattern but also the time pattern. It can make the trained network have nonlinear and dynamic characteristics. Based on the Elman-Markov model, a new method for predicting the surface settlement of deep foundation pit in pelagic division is proposed. Firstly, the wavelet de-noising method based on MATLAB is used to remove the observation error data (noise) and obtain the accurate data (real signal). Then the surface settlement prediction method based on the Elman network is used to predict the surface settlement of the deep foundation pit in the pelagic division. Finally, the Markov chain model is used to modify the prediction value to achieve the high-precision surface settlement prediction of the deep foundation pit in the pelagic division. It is proved that the method has high de-noising performance, high prediction accuracy, and high practicability. [ABSTRACT FROM AUTHOR] |
|
Copyright of Arabian Journal of Geosciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |
