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Data mining analysis of an urban tunnel pressure drop based on CFD data
conference contribution
posted on 2015-01-01, 00:00 authored by E Eftekharian, Seyedamin Khatami, Abbas KhosraviAbbas Khosravi, Saeid NahavandiAn 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.
History
Event
Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)Volume
9492Series
Lecture Notes in Computer SciencePagination
128 - 135Publisher
SpringerLocation
Istanbul, TurkeyPlace of publication
New York, N.Y.Publisher DOI
Start date
2015-11-09End date
2015-11-12ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319265605Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, SpringerTitle of proceedings
22nd International Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IVUsage metrics
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