Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145041
Title: Surface settlement modelling using neural network
Authors: Chen, Rongxing
Keywords: Engineering::Civil engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: GE49
Abstract: With the development of society, the utilization rate of underground space is getting higher and higher. Tunnel projects are gradually increasing. But settlement is the most common problem in tunnel engineering. Many scholars have conducted research on this issue, and they have developed many methods for predicting the settlement. But these methods have some shortcomings, that is, they are too complicated, and the accuracy of prediction is not high. This research uses neural network to predict tunnel settlement. The neural network model can process a large amount of information like a human brain and is very suitable for non-linear problems. In this study. There are two ways of neural network analysis. The first method is to find the best performing one by building 16 different hidden neurons models. The second method is to build models with different inputs. The models with 6 input performance best while model with 4 inputs is alternate optimal model.
URI: https://hdl.handle.net/10356/145041
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:CEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_CHENRONGXING_FINAL.pdf
  Restricted Access
5.37 MBAdobe PDFView/Open

Page view(s)

187
Updated on Jan 22, 2022

Download(s)

12
Updated on Jan 22, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.