You are not logged in.

Data mining analysis of an urban tunnel pressure drop based on CFD data

Eftekharian, Esmaeel, Khatami, Amin, Khosravi, Abbas and Nahavandi, Saeid 2015, Data mining analysis of an urban tunnel pressure drop based on CFD data, in 22nd International Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IV, Springer, New York, N.Y., pp. 128-135, doi: 10.1007/978-3-319-26561-2_16.

Attached Files
Name Description MIMEType Size Downloads

Title Data mining analysis of an urban tunnel pressure drop based on CFD data
Author(s) Eftekharian, Esmaeel
Khatami, Amin
Khosravi, Abbas
Nahavandi, Saeid
Conference name Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 9-12 Nov. 2015
Title of proceedings 22nd International Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IV
Publication date 2015
Series Lecture Notes in Computer Science v.9492
Start page 128
End page 135
Total pages 8
Publisher Springer
Place of publication New York, N.Y.
Summary An accurate estimation of pressure drop due to vehicles inside an urban tunnel plays a pivotal role in tunnel ventilation issue. The main aim of the present study is to utilize computational intelligence technique for predicting pressure drop due to cars in traffic congestion in urban tunnels. A supervised feed forward back propagation neural network is utilized to estimate this pressure drop. The performance of the proposed network structure is examined on the dataset achieved from Computational Fluid Dynamic (CFD) simulation. The input data includes 2 variables, tunnel velocity and tunnel length, which are to be imported to the corresponding algorithm in order to predict presure drop. 10-fold Cross validation technique is utilized for three data mining methods, namely: multi-layer perceptron algorithm, support vector machine regression, and linear regression. A comparison is to be made to show the most accurate results. Simulation results illustrate that the Multi-layer perceptron algorithm is able to accurately estimate the pressure drop.
ISBN 9783319265605
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-26561-2_16
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
08 Information And Computing Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082489

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
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 118 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Mon, 18 Apr 2016, 12:14:05 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.