Low-rise gable roof buildings pressure prediction using deep neural networks-Reference-Cited by-同舟云学术

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Low-rise gable roof buildings pressure prediction using deep neural networks

Published:2020-01 Issue: Volume:196 Page:104026
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:

Tian Jianqiao,Gurley Kurtis R.,Diaz Maximillian T.^ORCID,Fernández-Cabán Pedro L.,Masters Forrest J.^ORCID,Fang Ruogu

Funder

National Science Foundation

NHERI

NIH

National Center for Advancing Translational Sciences

University of Florida Informatics Institute Junior SEED Program

Publisher

Elsevier BV

Subject

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

Reference29 articles.

1. Performance comparison of svm and ann in predicting compressive strength of concrete;Akande;IOSR J. Comput. Eng.,2014

2. Cooling load prediction for buildings using general regression neural networks;Ben-Nakhi;Energy Convers. Manag.,2004

3. Prediction of wind pressure coefficients on building surfaces using artificial neural networks;Bre;Energy Build.,2018

4. Interpolation of wind-induced pressure time series with an artificial neural network;Chen;J. Wind Eng. Ind. Aerodyn.,2002

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

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