Open Access Journal

ISSN : 2394-2320 (Online)

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

Monthly Journal for Computer Science and Engineering

Open Access Journal

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

Monthly Journal for Mechanical and Civil Engineering

ISSN : 2456-1290 (Online)

Use of Artificial Neural Network in Wind Response of Tall Buildings

Author : Suyog U. Dhote 1 Valsson Varghese 2

Date of Publication :9th March 2017

Abstract: India is the second largest country in the world with about 1,336,087,445 population as on today. In present scenario, population density in our country reaches approximately 455 per square kilometers and in a way to grow at higher side. As we all know the basic requirement for an individual is food, shelter and water to survive. A civil engineer plays an important role in providing shelter to each and every citizen of India. Presently population growth is a main hurdle for an engineer to come across and space in big cities is one of the big task for civil engineers. With the lack of space engineers find their way in designing the slender, taller structures. With the increase in height of building, the study of wind induced building motion becomes very important. Wind tunnel experiments are the basic source to study these motions for taller buildings. But use of wind tunnel is not feasible every time and it is must to find alternate solutions. I.S. 875 (Part - 3): 1987 describes along wind response by gust factor method by considering the effects of change in terrain category. A new code reaffirmed in 2013 is also available to calculate along and across response of buildings. This paper discusses the method for calculating along wind response with use of Indian standard codes and Artificial Neural Network to save the time and money required in wind tunnel experiments.

Reference :

    1. 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.
    2. 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.
    3. James L. Rogers, Simulating Analysis with Neural Network, Journal of Computing in Civil Engineering, 8(2), 1994, Paper No. 5286.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Bodhisatta Hazra, Along Wind Response of Tall Buildings, M. Tech. Project, Department of Applied Mechanics, 2007, VNIT, Nagpur.
    9. Suyog U. Dhote, P. N. Godbole (2012), “Wind Response of Tall Multistoried Buildings”, M. Tech. Thesis, RCOEM, Nagpur.

Recent Article