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author:

Liu, Qiang (Liu, Qiang.) [1] | Yang, Chun-Yan (Yang, Chun-Yan.) [2] | Lin, Li (Lin, Li.) [3]

Indexed by:

EI

Abstract:

The purpose of this study was to predict the deformation of a deep foundation pit based on a combination model of wavelet transform and gray BP neural network. Using a case of a deep foundation pit, a combination model of wavelet transform and gray BP neural network was used to predict the deformation of the deep foundation pit. The results show that compared with the traditional gray BP neural network model, the relative error of the combination model of wavelet transform and gray BP neural network was reduced by 2.38%. This verified that the combined model has high accuracy and reliability in the prediction of foundation pit deformation and also conforms to the actual situation of the project. The research results can provide a valuable reference for foundation pit deformation monitoring. © 2021 Qiang Liu et al.

Keyword:

Deformation Forecasting Foundations Neural networks Wavelet transforms

Community:

  • [ 1 ] [Liu, Qiang]College of Harbour and Coastal Engineering, Jimei University, Xiamen; 361021, China
  • [ 2 ] [Yang, Chun-Yan]College of Harbour and Coastal Engineering, Jimei University, Xiamen; 361021, China
  • [ 3 ] [Lin, Li]College of Civil Engineering, Fuzhou University, Fuzhou; 350007, China

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Source :

Mathematical Problems in Engineering

ISSN: 1024-123X

Year: 2021

Volume: 2021

1 . 4 3

JCR@2021

1 . 4 3 0

JCR@2021

JCR Journal Grade:3

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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