Surface settlement modelling using neural network 1

Ground movement control during tunnelling in urban areas has always been a key concern, as geotechnical engineers strive to minimize any disturbance to nearby buildings and services. Previous studies concerning surface settlement were conducted based on empirical and analytical methods, and both we...

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Main Author: Tan, Jerie-Ann
Other Authors: Zhao Zhiye
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/151402
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1514022023-03-03T17:30:06Z Surface settlement modelling using neural network 1 Tan, Jerie-Ann Zhao Zhiye School of Civil and Environmental Engineering CZZHAO@ntu.edu.sg Engineering::Civil engineering Ground movement control during tunnelling in urban areas has always been a key concern, as geotechnical engineers strive to minimize any disturbance to nearby buildings and services. Previous studies concerning surface settlement were conducted based on empirical and analytical methods, and both were limited in accurately predicting the extent of surface settlement because they do not consider the complex ground conditions.This research was carried out to investigate the effects of various input parameters on surface settlement due to tunnelling work. Geological parameters and mechanical properties of Tunnel Boring Machines(TBMs)used had been obtained from three tunnelling projects inSingapore. A sensitivity analysis was conducted to determine the influence of each input parameter on the surface settlement. Subsequently, an Artificial Neural Network (ANN) model was developed for the prediction of surface settlementbased on the chosen input parameters. The modelling work had been performed under different combinations of input parameters and hiddennodes, andthe model with the highest accuracy will eventually be used for the prediction of surface settlement based on the inputs given. Bachelor of Engineering (Civil) 2021-06-16T09:32:24Z 2021-06-16T09:32:24Z 2021 Final Year Project (FYP) Tan, J. (2021). Surface settlement modelling using neural network 1. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151402 https://hdl.handle.net/10356/151402 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering
spellingShingle Engineering::Civil engineering
Tan, Jerie-Ann
Surface settlement modelling using neural network 1
description Ground movement control during tunnelling in urban areas has always been a key concern, as geotechnical engineers strive to minimize any disturbance to nearby buildings and services. Previous studies concerning surface settlement were conducted based on empirical and analytical methods, and both were limited in accurately predicting the extent of surface settlement because they do not consider the complex ground conditions.This research was carried out to investigate the effects of various input parameters on surface settlement due to tunnelling work. Geological parameters and mechanical properties of Tunnel Boring Machines(TBMs)used had been obtained from three tunnelling projects inSingapore. A sensitivity analysis was conducted to determine the influence of each input parameter on the surface settlement. Subsequently, an Artificial Neural Network (ANN) model was developed for the prediction of surface settlementbased on the chosen input parameters. The modelling work had been performed under different combinations of input parameters and hiddennodes, andthe model with the highest accuracy will eventually be used for the prediction of surface settlement based on the inputs given.
author2 Zhao Zhiye
author_facet Zhao Zhiye
Tan, Jerie-Ann
format Final Year Project
author Tan, Jerie-Ann
author_sort Tan, Jerie-Ann
title Surface settlement modelling using neural network 1
title_short Surface settlement modelling using neural network 1
title_full Surface settlement modelling using neural network 1
title_fullStr Surface settlement modelling using neural network 1
title_full_unstemmed Surface settlement modelling using neural network 1
title_sort surface settlement modelling using neural network 1
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/151402
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