Deformation Prediction of a Deep Foundation Pit Based on the Combination Model of Wavelet Transform and Gray BP Neural Network | Zendy

Liu Qiang | Zendy; Chunyan Yang | Zendy; Lin Li | Zendy

Open Access

Deformation Prediction of a Deep Foundation Pit Based on the Combination Model of Wavelet Transform and Gray BP Neural Network

Author(s) -

Liu Qiang,

Chunyan Yang,

Lin Li

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/2161254

Subject(s) - artificial neural network , artificial intelligence , wavelet transform , foundation (evidence) , gray (unit) , deformation (meteorology) , pattern recognition (psychology) , wavelet , computer science , engineering , structural engineering , geology , geography , medicine , oceanography , archaeology , radiology

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.

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