Concrete box girder bridge design using neural network
The box girder is the most widely used superstructure in concrete bridge construction. Because of its desirable characteristics, concrete box girder bridges have been widely used as an economic and aesthetic solution for overcrossings, separation structures and viaducts found in today's modern...
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sg-ntu-dr.10356-122622023-03-03T19:16:49Z Concrete box girder bridge design using neural network He, Wen Wei. Zhao, Zhiye School of Civil and Structural Engineering DRNTU::Engineering::Civil engineering::Structures and design The box girder is the most widely used superstructure in concrete bridge construction. Because of its desirable characteristics, concrete box girder bridges have been widely used as an economic and aesthetic solution for overcrossings, separation structures and viaducts found in today's modern highway systems. Master of Engineering (CSE) 2008-09-25T06:41:56Z 2008-09-25T06:41:56Z 1999 1999 Thesis http://hdl.handle.net/10356/12262 en Nanyang Technological University 251 p. application/pdf |
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DRNTU::Engineering::Civil engineering::Structures and design He, Wen Wei. Concrete box girder bridge design using neural network |
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The box girder is the most widely used superstructure in concrete bridge construction. Because of its desirable characteristics, concrete box girder bridges have been widely used as an economic and aesthetic solution for overcrossings, separation structures and viaducts found in today's modern highway systems. |
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Zhao, Zhiye |
author_facet |
Zhao, Zhiye He, Wen Wei. |
format |
Theses and Dissertations |
author |
He, Wen Wei. |
author_sort |
He, Wen Wei. |
title |
Concrete box girder bridge design using neural network |
title_short |
Concrete box girder bridge design using neural network |
title_full |
Concrete box girder bridge design using neural network |
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Concrete box girder bridge design using neural network |
title_full_unstemmed |
Concrete box girder bridge design using neural network |
title_sort |
concrete box girder bridge design using neural network |
publishDate |
2008 |
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http://hdl.handle.net/10356/12262 |
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1759854737604214784 |