PREDICTING WIND PRESSURE ON LOWRISE BUILDINGS WITH PROTECTIVE PARAPETS USING ARTIFICIAL NEURAL NETWORKS

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dc.contributor.advisorPhillips, Brian Men_US
dc.contributor.authorWang, Yananen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-07-14T05:35:40Z
dc.date.available2020-07-14T05:35:40Z
dc.date.issued2020en_US
dc.description.abstractWind hazards cause tremendous destruction and threaten people’s safety and economical losses. To improve current provision toward wind load and strengthen buildings against wind forces, it’s vital to properly characterize wind loading on buildings in wind hazard. The turbulence created by the bluff body makes analytical modeling difficult. Therefore, engineers typically turn to wind tunnel tests. This thesis investigates the application of Artificial Neural Networks (ANN) to predict the wind pressure on low-rise buildings with protective parapets. With existing experimental datasets conducted in BLWT, ANN models were trained to model non-linear relationship between inputs, such as tap coordinates and parapet height, and outputs, such as pressure coefficients. The developed model was used to predict pressure coefficients with unseen parapet height to cut down experimental cost.en_US
dc.identifierhttps://doi.org/10.13016/wl9k-zksw
dc.identifier.urihttp://hdl.handle.net/1903/26318
dc.language.isoenen_US
dc.subject.pqcontrolledCivil engineeringen_US
dc.subject.pquncontrolledartificial neural networken_US
dc.subject.pquncontrolledbackpropagationen_US
dc.subject.pquncontrolledlow rise buildingen_US
dc.subject.pquncontrolledpressure coefficientsen_US
dc.titlePREDICTING WIND PRESSURE ON LOWRISE BUILDINGS WITH PROTECTIVE PARAPETS USING ARTIFICIAL NEURAL NETWORKSen_US
dc.typeThesisen_US

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