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|Title:||Surface settlement modelling using neural network 1||Authors:||Tan, Jerie-Ann||Keywords:||Engineering::Civil engineering||Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Tan, J. (2021). Surface settlement modelling using neural network 1. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151402||Abstract:||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.||URI:||https://hdl.handle.net/10356/151402||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||CEE Student Reports (FYP/IA/PA/PI)|
Updated on Aug 2, 2021
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