Mostrar el registro sencillo del ítem

dc.contributor.author
Bre, Facundo
dc.contributor.author
Gimenez, Juan Marcelo
dc.contributor.author
Fachinotti, Victor Daniel
dc.date.available
2019-10-17T20:50:48Z
dc.date.issued
2018-01
dc.identifier.citation
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
dc.identifier.issn
0378-7788
dc.identifier.uri
http://hdl.handle.net/11336/86215
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science Sa
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
ARTIFICIAL NEURAL NETWORK
dc.subject
BUILDING PERFORMANCE SIMULATION
dc.subject
NATURAL VENTILATION
dc.subject
PRESSURE COEFFICIENT
dc.subject.classification
Ingeniería Civil
dc.subject.classification
Ingeniería Civil
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.subject.classification
Mecánica Aplicada
dc.subject.classification
Ingeniería Mecánica
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Prediction of wind pressure coefficients on building surfaces using artificial neural networks
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-16T19:29:15Z
dc.journal.volume
158
dc.journal.pagination
1429-1441
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
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
dc.description.fil
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
dc.description.fil
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
dc.journal.title
Energy and Buildings
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.enbuild.2017.11.045
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378778817325501


Archivos asociados

Documento no disponible

Comunidades y colecciones

  • Articulos(CIMEC) [130]
    Articulos de CENTRO DE INVESTIGACION DE METODOS COMPUTACIONALES

Mostrar el registro sencillo del ítem