Wavelet prediction method for ground deformation induced by tunneling

Jian Guo, Lieyun Ding, Hanbin Luo, Cheng Zhou, Ling Ma

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

A wavelet intelligence prediction system (WIPS) is presented herein to predict the ground deformations induced by tunneling. In this method, the solution is comprised of three parts: wavelet analysis, model identification and system prediction. Based on the sensitivity analysis of influencing factors, ground deformation is decomposed into the trend deformation and the wave deformation. Wavelet analysis is introduced to filter the residual error and extract the actual deformations, which is similar to de-noising in signal processing. In addition, the identification model is established by using Elman neural network based on modified PSO (named EMPIM), with which one can approximate the actual deformations. The prediction system (i.e.; WIPS) developed with two identifiers enable one to map all influencing parameters to ground deformations, which helps avoid complex theoretical analysis of rock-soil mechanisms and mathematical descriptions of ground deformations. Later, WIPS is applied to estimate future deformations. The validation use cases show that the WIPS is an effective tool for predicting ground deformations dynamically under difficult and uncertain conditions, and can be widely applied to practical subway projects.

Original languageEnglish
Pages (from-to)137-151
Number of pages15
JournalTunnelling and Underground Space Technology
Volume41
Early online date22 Jan 2014
DOIs
Publication statusPublished - Mar 2014
Externally publishedYes

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wavelet
prediction
Wavelet analysis
wavelet analysis
Identification (control systems)
method
Subways
signal processing
Particle swarm optimization (PSO)
Sensitivity analysis
sensitivity analysis
Signal processing
Rocks
filter
Neural networks
Soils
rock

Cite this

Guo, Jian ; Ding, Lieyun ; Luo, Hanbin ; Zhou, Cheng ; Ma, Ling. / Wavelet prediction method for ground deformation induced by tunneling. In: Tunnelling and Underground Space Technology. 2014 ; Vol. 41. pp. 137-151.
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Wavelet prediction method for ground deformation induced by tunneling. / Guo, Jian; Ding, Lieyun; Luo, Hanbin; Zhou, Cheng; Ma, Ling.

In: Tunnelling and Underground Space Technology, Vol. 41, 03.2014, p. 137-151.

Research output: Contribution to journalArticle

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T1 - Wavelet prediction method for ground deformation induced by tunneling

AU - Guo, Jian

AU - Ding, Lieyun

AU - Luo, Hanbin

AU - Zhou, Cheng

AU - Ma, Ling

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N2 - A wavelet intelligence prediction system (WIPS) is presented herein to predict the ground deformations induced by tunneling. In this method, the solution is comprised of three parts: wavelet analysis, model identification and system prediction. Based on the sensitivity analysis of influencing factors, ground deformation is decomposed into the trend deformation and the wave deformation. Wavelet analysis is introduced to filter the residual error and extract the actual deformations, which is similar to de-noising in signal processing. In addition, the identification model is established by using Elman neural network based on modified PSO (named EMPIM), with which one can approximate the actual deformations. The prediction system (i.e.; WIPS) developed with two identifiers enable one to map all influencing parameters to ground deformations, which helps avoid complex theoretical analysis of rock-soil mechanisms and mathematical descriptions of ground deformations. Later, WIPS is applied to estimate future deformations. The validation use cases show that the WIPS is an effective tool for predicting ground deformations dynamically under difficult and uncertain conditions, and can be widely applied to practical subway projects.

AB - A wavelet intelligence prediction system (WIPS) is presented herein to predict the ground deformations induced by tunneling. In this method, the solution is comprised of three parts: wavelet analysis, model identification and system prediction. Based on the sensitivity analysis of influencing factors, ground deformation is decomposed into the trend deformation and the wave deformation. Wavelet analysis is introduced to filter the residual error and extract the actual deformations, which is similar to de-noising in signal processing. In addition, the identification model is established by using Elman neural network based on modified PSO (named EMPIM), with which one can approximate the actual deformations. The prediction system (i.e.; WIPS) developed with two identifiers enable one to map all influencing parameters to ground deformations, which helps avoid complex theoretical analysis of rock-soil mechanisms and mathematical descriptions of ground deformations. Later, WIPS is applied to estimate future deformations. The validation use cases show that the WIPS is an effective tool for predicting ground deformations dynamically under difficult and uncertain conditions, and can be widely applied to practical subway projects.

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KW - Sensitivity analysis

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