OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK

The use of castellated beam in construction are now applied widely, because it has advantages, such as capacity of the larger bending moment because of adding to high beams without adding to the self weight, a beautiful artistic value is compared originial I-beam and the hole can be used for mechani...

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Main Authors: , AHMAD MUHTAROM, , Akhmad Aminullah, S.T., M.T., Ph.D.
Format: Theses and Dissertations NonPeerReviewed
Published: [Yogyakarta] : Universitas Gadjah Mada 2012
Subjects:
ETD
Online Access:https://repository.ugm.ac.id/100954/
http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=57380
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Institution: Universitas Gadjah Mada
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spelling id-ugm-repo.1009542016-03-04T08:45:14Z https://repository.ugm.ac.id/100954/ OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK , AHMAD MUHTAROM , Akhmad Aminullah, S.T., M.T., Ph.D. ETD The use of castellated beam in construction are now applied widely, because it has advantages, such as capacity of the larger bending moment because of adding to high beams without adding to the self weight, a beautiful artistic value is compared originial I-beam and the hole can be used for mechanicalelectrical installations. Besides having advantages castellated beam also have some disadvantages that the shear force and buckling because of some modifications. To reduce these disadvantages should be optimized dimensionalhexagonal hole to be able to carry the load optimally. This study was conducted in two stages, the first stage to make numerical models with hexagonal hole dimensional variations use finite element method analysis. The second stage to perform simulation result of analysis of numerical models using Artificial Neural Network to obtain a mathematical function used as a prediction of the most optimal hole dimensions. The input parameters used in the mathematical function is depth and width of the hexagonal holes while output parameters is stress and deformation that occur on the castellated beam. The result of study shows that the higher and the wider hexagonal hole, so the larger stress on the web and the smaller the ratio of hole, so the smaller shear force. The result of obtained generalization of data using Artificial Neural Network to target output is close to finite element method and its average ratio is 1.01. The result of obtained also using Artificial Neural Network simulation, the most optimal-hexagonal hole the case study castellated beam 225x75x7x5 mm dimension with span of 1 meter and 2 points load amount of 400 kN are : depth of hole = 100 mm, dept of stem = 62.5 mm, width of web post = 80 mm and width of hole = 80 mm. [Yogyakarta] : Universitas Gadjah Mada 2012 Thesis NonPeerReviewed , AHMAD MUHTAROM and , Akhmad Aminullah, S.T., M.T., Ph.D. (2012) OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=57380
institution Universitas Gadjah Mada
building UGM Library
country Indonesia
collection Repository Civitas UGM
topic ETD
spellingShingle ETD
, AHMAD MUHTAROM
, Akhmad Aminullah, S.T., M.T., Ph.D.
OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK
description The use of castellated beam in construction are now applied widely, because it has advantages, such as capacity of the larger bending moment because of adding to high beams without adding to the self weight, a beautiful artistic value is compared originial I-beam and the hole can be used for mechanicalelectrical installations. Besides having advantages castellated beam also have some disadvantages that the shear force and buckling because of some modifications. To reduce these disadvantages should be optimized dimensionalhexagonal hole to be able to carry the load optimally. This study was conducted in two stages, the first stage to make numerical models with hexagonal hole dimensional variations use finite element method analysis. The second stage to perform simulation result of analysis of numerical models using Artificial Neural Network to obtain a mathematical function used as a prediction of the most optimal hole dimensions. The input parameters used in the mathematical function is depth and width of the hexagonal holes while output parameters is stress and deformation that occur on the castellated beam. The result of study shows that the higher and the wider hexagonal hole, so the larger stress on the web and the smaller the ratio of hole, so the smaller shear force. The result of obtained generalization of data using Artificial Neural Network to target output is close to finite element method and its average ratio is 1.01. The result of obtained also using Artificial Neural Network simulation, the most optimal-hexagonal hole the case study castellated beam 225x75x7x5 mm dimension with span of 1 meter and 2 points load amount of 400 kN are : depth of hole = 100 mm, dept of stem = 62.5 mm, width of web post = 80 mm and width of hole = 80 mm.
format Theses and Dissertations
NonPeerReviewed
author , AHMAD MUHTAROM
, Akhmad Aminullah, S.T., M.T., Ph.D.
author_facet , AHMAD MUHTAROM
, Akhmad Aminullah, S.T., M.T., Ph.D.
author_sort , AHMAD MUHTAROM
title OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK
title_short OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK
title_full OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK
title_fullStr OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK
title_full_unstemmed OPTIMASI DIMENSI LUBANG HEKSAGONAL BALOK KASTELA BENTANG PENDEK DENGAN METODE ARTIFICIAL NEURAL NETWORK
title_sort optimasi dimensi lubang heksagonal balok kastela bentang pendek dengan metode artificial neural network
publisher [Yogyakarta] : Universitas Gadjah Mada
publishDate 2012
url https://repository.ugm.ac.id/100954/
http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=57380
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