Prediction of wind pressure coefficients on building surfaces using artificial neural networks

Autores
Bre, Facundo; Gimenez, Juan Marcelo; Fachinotti, Victor Daniel
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Knowing the pressure coefficient on building surfaces is important for the evaluation of wind loads and natural ventilation. The main objective of this paper is to present and to validate a computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings. This approach makes use of artificial neural network (ANN) to estimate the surface-average pressure coefficient for each wall and roof according to the building geometry and the wind angle. Three separate ANN models were developed, one for each roof type, and trained using an experimental database. Applied to a wide variety of buildings, the current ANN models were proved to be considerably more accurate than the commonly used parametric equations for the estimation of pressure coefficients. The proposed ANN-based methodology is as general and versatile as to be easily expanded to buildings with different shapes as well as to be coupled to building performance simulation and airflow network programs.
Fil: Bre, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Gimenez, Juan Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Fachinotti, Victor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Materia
ARTIFICIAL NEURAL NETWORK
BUILDING PERFORMANCE SIMULATION
NATURAL VENTILATION
PRESSURE COEFFICIENT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/86215
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/86215
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Prediction of wind pressure coefficients on building surfaces using artificial neural networksBre, FacundoGimenez, Juan MarceloFachinotti, Victor DanielARTIFICIAL NEURAL NETWORKBUILDING PERFORMANCE SIMULATIONNATURAL VENTILATIONPRESSURE COEFFICIENThttps://purl.org/becyt/ford/2.1https://purl.org/becyt/ford/2https://purl.org/becyt/ford/2.3https://purl.org/becyt/ford/2Knowing the pressure coefficient on building surfaces is important for the evaluation of wind loads and natural ventilation. The main objective of this paper is to present and to validate a computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings. This approach makes use of artificial neural network (ANN) to estimate the surface-average pressure coefficient for each wall and roof according to the building geometry and the wind angle. Three separate ANN models were developed, one for each roof type, and trained using an experimental database. Applied to a wide variety of buildings, the current ANN models were proved to be considerably more accurate than the commonly used parametric equations for the estimation of pressure coefficients. The proposed ANN-based methodology is as general and versatile as to be easily expanded to buildings with different shapes as well as to be coupled to building performance simulation and airflow network programs.Fil: Bre, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Gimenez, Juan Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Fachinotti, Victor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaElsevier Science Sa2018-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/86215Bre, Facundo; Gimenez, Juan Marcelo; Fachinotti, Victor Daniel; Prediction of wind pressure coefficients on building surfaces using artificial neural networks; Elsevier Science Sa; Energy and Buildings; 158; 1-2018; 1429-14410378-7788CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.enbuild.2017.11.045info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378778817325501info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:13:52Zoai:ri.conicet.gov.ar:11336/86215instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:13:53.191CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Prediction of wind pressure coefficients on building surfaces using artificial neural networks
title Prediction of wind pressure coefficients on building surfaces using artificial neural networks
spellingShingle Prediction of wind pressure coefficients on building surfaces using artificial neural networks
Bre, Facundo
ARTIFICIAL NEURAL NETWORK
BUILDING PERFORMANCE SIMULATION
NATURAL VENTILATION
PRESSURE COEFFICIENT
title_short Prediction of wind pressure coefficients on building surfaces using artificial neural networks
title_full Prediction of wind pressure coefficients on building surfaces using artificial neural networks
title_fullStr Prediction of wind pressure coefficients on building surfaces using artificial neural networks
title_full_unstemmed Prediction of wind pressure coefficients on building surfaces using artificial neural networks
title_sort Prediction of wind pressure coefficients on building surfaces using artificial neural networks
dc.creator.none.fl_str_mv Bre, Facundo
Gimenez, Juan Marcelo
Fachinotti, Victor Daniel
author Bre, Facundo
author_facet Bre, Facundo
Gimenez, Juan Marcelo
Fachinotti, Victor Daniel
author_role author
author2 Gimenez, Juan Marcelo
Fachinotti, Victor Daniel
author2_role author
author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL NETWORK
BUILDING PERFORMANCE SIMULATION
NATURAL VENTILATION
PRESSURE COEFFICIENT
topic ARTIFICIAL NEURAL NETWORK
BUILDING PERFORMANCE SIMULATION
NATURAL VENTILATION
PRESSURE COEFFICIENT
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.1
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/2.3
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Knowing the pressure coefficient on building surfaces is important for the evaluation of wind loads and natural ventilation. The main objective of this paper is to present and to validate a computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings. This approach makes use of artificial neural network (ANN) to estimate the surface-average pressure coefficient for each wall and roof according to the building geometry and the wind angle. Three separate ANN models were developed, one for each roof type, and trained using an experimental database. Applied to a wide variety of buildings, the current ANN models were proved to be considerably more accurate than the commonly used parametric equations for the estimation of pressure coefficients. The proposed ANN-based methodology is as general and versatile as to be easily expanded to buildings with different shapes as well as to be coupled to building performance simulation and airflow network programs.
Fil: Bre, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Gimenez, Juan Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Fachinotti, Victor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
description Knowing the pressure coefficient on building surfaces is important for the evaluation of wind loads and natural ventilation. The main objective of this paper is to present and to validate a computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings. This approach makes use of artificial neural network (ANN) to estimate the surface-average pressure coefficient for each wall and roof according to the building geometry and the wind angle. Three separate ANN models were developed, one for each roof type, and trained using an experimental database. Applied to a wide variety of buildings, the current ANN models were proved to be considerably more accurate than the commonly used parametric equations for the estimation of pressure coefficients. The proposed ANN-based methodology is as general and versatile as to be easily expanded to buildings with different shapes as well as to be coupled to building performance simulation and airflow network programs.
publishDate 2018
dc.date.none.fl_str_mv 2018-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/86215
Bre, Facundo; Gimenez, Juan Marcelo; Fachinotti, Victor Daniel; Prediction of wind pressure coefficients on building surfaces using artificial neural networks; Elsevier Science Sa; Energy and Buildings; 158; 1-2018; 1429-1441
0378-7788
CONICET Digital
CONICET
url http://hdl.handle.net/11336/86215
identifier_str_mv Bre, Facundo; Gimenez, Juan Marcelo; Fachinotti, Victor Daniel; Prediction of wind pressure coefficients on building surfaces using artificial neural networks; Elsevier Science Sa; Energy and Buildings; 158; 1-2018; 1429-1441
0378-7788
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.enbuild.2017.11.045
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378778817325501
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Sa
publisher.none.fl_str_mv Elsevier Science Sa
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846781552533438464
score 12.982451

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