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International Conference on Neural Information Processing

ICONIP 2015: Neural Information Processing pp 128–135Cite as

Data Mining Analysis of an Urban Tunnel Pressure Drop Based on CFD Data

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9492))

Abstract

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.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Shiraz University, Mollasadra Street, Shiraz, Iran

    Esmaeel Eftekharian

  2. Centre for Intelligent Systems Research, Deakin University, Geelong, VIC, 3216, Australia

    Amin Khatami, Abbas Khosravi & Saeid Nahavandi

Authors
  1. Esmaeel Eftekharian
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  2. Amin Khatami
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  3. Abbas Khosravi
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  4. Saeid Nahavandi
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Corresponding author

Correspondence to Amin Khatami .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Eftekharian, E., Khatami, A., Khosravi, A., Nahavandi, S. (2015). Data Mining Analysis of an Urban Tunnel Pressure Drop Based on CFD Data. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-26561-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26560-5

  • Online ISBN: 978-3-319-26561-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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