Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/12217
Title: Prediction of ground settlement due to tunneling using artifical neural networks
Authors: Dhony Kurniawan Hidayat
Keywords: DRNTU::Engineering::Civil engineering::Geotechnical
Issue Date: 2006
Source: Dhony, K. H. (2006). Prediction of ground settlement due to tunneling using artifical neural networks. Master’s thesis, Nanyang Technological University, Singapore.
Abstract: Ground surface settlement trough associated to tunneling is characterized by two important parameters: the maximum surface settlement at the point above the tunnel centerline (Smax) and the width parameter (i) which is defined as the distance from the tunnel centerline to the inflection point of the trough. The estimation of these settlement parameters is a very complex problem due to uncertain nature of the soil. Over the years, many methods have been proposed to predict the tunneling-induced settlements. Most of these methods are empirical in nature. However, a method with high degree of accuracy and consistency has not yet been developed. Accurate prediction of settlement is essential since settlement is the governing factor in the design process of the tunnels. In this research, the use of artificial neural network (ANN) for the prediction of maximum surface settlement and trough width is explored.
URI: https://hdl.handle.net/10356/12217
DOI: 10.32657/10356/12217
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Theses

Files in This Item:
File Description SizeFormat 
CEE-THESES_56.pdf15.92 MBAdobe PDFThumbnail
View/Open

Page view(s) 20

400
Updated on Feb 27, 2021

Download(s) 10

272
Updated on Feb 27, 2021

Google ScholarTM

Check

Altmetric


Plumx

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