Open Access Journal

ISSN : 2456-1290 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Mechanical and Civil Engineering (IJERMCE)

Monthly Journal for Mechanical and Civil Engineering

ISSN : 2456-1290 (Online)

Application of Artificial Neural Network in Wind Response of Tall Buildings

Author : Suyog U. Dhote 1 Valsson Varghese 2

Date of Publication :24th January 2018

Abstract: As per World Population Prospects 2017, India is having about 1.3 billion population and ranks second in the world. Due to the continuous increase in population, lack of open spaces plays a very vital role in growing economies. With the lack of open spaces, tall buildings are featuring well in developed as well as developing countries. With the increase in demand of tall buildings, it is a basic need to do the analysis of the tall buildings considering the dynamic response of a tall structure subjected to wind. The Indian code of practice IS 875 (Part-3):2015 gives the procedure to determine along and across wind response of tall structures. Artificial Neural Network approach in wind response of tall buildings is very useful and rapid method where availability of major data is critical.

Reference :

    1. IS 875 (Part 3): 1987, code of Practice for Design Loads (other than Earthquake) for Buildings and structures – Part 3 Wind loads, Bureau of Indian Standards, Manak Bhawan, New Delhi.
    2. IS 875 (Part 3): 2015, code of Practice for Design Loads (other than Earthquake) for Buildings and structures – Part 3 Wind loads, Bureau of Indian Standards, Manak Bhawan, New Delhi.
    3. Flood Ian and Kartam Nabil, Neural Networks in Civil Engineering. I: Principles and understanding, Journal of Computing in Civil Engineering, 8(2), 1994, pp 131 – 147.
    4. Girma T. and Godbole P. N. , Application of Cascade Correlation Learning Network for Determining Wind Pressure Distribution on Building, Proceedings 10 ICWE, Copenhegan, Denmark, June 21 – 24, 1991, pp 1491 – 1496.
    5.  James L. Rogers, Simulating Analysis with Neural Network, Journal of Computing in Civil Engineering, 8(2), 1994, Paper No. 5286.
    6.  Khanduri A.C., Bedard C., Stathopouls T., Neural Network Modeling of Wind Induced Interference Effects, Proceedings 9 ICWE, New Delhi, India, 1995, pp 1341 – 1352.
    7. Kwatra N., Godbole P. N., Premkrishna, Application of ANN for Determination of Wind Induced Pressure on Gable Roof, International Journal of Wind and Strustures, 5(1), 2002, pp 1 – 14.
    8. Prem Krishna, Krishen Kumar, N. M. Bhandari, IS: 1987 (Part 3): Wind Loads on Buildings and Structures – Proposed Draft & Commentary Diocument No. IITK – GSDMA – Wind 02 – V5.0, 2004, IITK – GSDMA project on building codes, Department of Civil Engineering, IIT Kanpur, India.
    9. Rao M. M. and Datta T. K., Modal Seismic Control of Building Frames by Artificial Neural Network, Journal of Computing in Civil Engineering, 20(1), 2006, pp 69 – 73.
    10. Bodhisatta Hazra, Along Wind Response of Tall Buildings, M. Tech. Project, Department of Applied Mechanics, 2007, VNIT, Nagpur.
    11. Kanchan Patil, Application of ANN for wind loads in buildings and structures, M. Tech. Project, Department of Applied Mechanics, 2008, VNIT, Nagpur.

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