Artificial neural network analysis of twin tunnelling-induced ground settlements

Khatami, Seyed A., Mirhabibi, Alireza, Khosravi, Abbas and Nahavandi, Saeid 2013, Artificial neural network analysis of twin tunnelling-induced ground settlements, in SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 2492-2498.

Attached Files
Name Description MIMEType Size Downloads

Title Artificial neural network analysis of twin tunnelling-induced ground settlements
Author(s) Khatami, Seyed A.
Mirhabibi, Alireza
Khosravi, Abbas
Nahavandi, Saeid
Conference name IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)
Conference location Manchester, England
Conference dates 13-16 Oct. 2013
Title of proceedings SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Systems, Man and Cybernetics Conference
Start page 2492
End page 2498
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) neural network
supervised learning
twin tunnel
tunnel-building interaction
surface settlement
Summary In this paper, we apply a computational intelligence method for tunnelling settlement prediction. A supervised feed forward back propagation neural network is used to predict the surface settlement during twin-tunnelling while surface buildings are considered in the models. The performance of the statistical neural network structure is tested on a dataset provided by numerical parametric studies conducted by ABAQUS software based on Shiraz line 1 metro data. Six input variables are fed to neural network model for predicting the surface settlement. These include tunnel center depth, distance between centerlines of twin tunnels, buildings width and building bending stiffness, and building weight and distance to tunnel centerline. Simulation results indicate that the proposed NN models are able to accurately predict the surface settlement.
ISBN 9781479906529
9780769551548
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058811

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 26 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Mon, 09 Dec 2013, 10:45:31 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.