Interpolation of pressure coefficients for low-rise buildings of different plan dimensions and roof slopes using artificial neural networks-Reference-Cited by-同舟云学术

Interpolation of pressure coefficients for low-rise buildings of different plan dimensions and roof slopes using artificial neural networks

Published:2011-05 Issue:5 Volume:99 Page:658-664
ISSN:0167-6105
Container-title:Journal of Wind Engineering and Industrial Aerodynamics
language:en
Short-container-title:Journal of Wind Engineering and Industrial Aerodynamics

Author:

Gavalda X.,Ferrer-Gener J.,Kopp Gregory A.,Giralt Francesc

Publisher

Elsevier BV

Subject

Mechanical Engineering,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering

Reference21 articles.

1. ANSI/ASCE-98: Minimum Design Loads for Buildings and Other Structures;American Society of Civil Engineers,1999

2. ASCE Standard, Minimum Design Loads for Buildings and Other Structures;American Society of Civil Engineers (ASCE),2002

3. Prediction of pressure coefficients on roofs of low buildings using artificial neural networks;Chen;J. Wind Eng. Ind. Aerodyn.,2003

4. Gavaldà, X., 2010. Predicció de coeficients de pressió mitjançant xarxes neuronals artificials, Ph.D. Thesis. Universitat Rovira i Virgili, Tarragona, Spain.

5. Neural Networks: A Comprehensive Foundation;Haykin,1994

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