Artificial Neural Network-based Designs of Prestressed Concrete and Composite Structures /
This book introduces artificial neural network (ANN)-based Lagrange optimization techniques for a structural design of prestressed concrete structures based on Eurocode 2, and composite structures based on American Institute of Steel Construction and American Concrete Institute standards. The book p...
| Main Authors: | , |
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| Format: | software, multimedia |
| Language: | eng |
| Published: |
Boca Raton, Florida : CRC Press,
2024
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| Subjects: | |
| Online Access: | https://www-taylorfrancis-com.ezproxy.utm.my/books/mono/10.1201/9781003354796/artificial-neural-network-based-designs-prestressed-concrete-composite-structures-won%E2%80%90kee-hong |
| _version_ | 1826473541161189376 |
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| author | Hong, Won‐Kee author 654926 Taylor & Francis Group (Online service) 645650 |
| author_facet | Hong, Won‐Kee author 654926 Taylor & Francis Group (Online service) 645650 |
| author_sort | Hong, Won‐Kee author 654926 |
| collection | OCEAN |
| description | This book introduces artificial neural network (ANN)-based Lagrange optimization techniques for a structural design of prestressed concrete structures based on Eurocode 2, and composite structures based on American Institute of Steel Construction and American Concrete Institute standards. The book provides robust design charts for prestressed concrete structures, which are challenging to achieve using conventional design methods. Using ANN-based design charts, the holistic design of a post-tensioned beam is performed to optimize design targets (objective functions), while calculating 21 forward outputs, in arbitrary sequences, from 21 forward inputs. Applies the powerful tools of ANN to the optimization of prestressed concrete structures and composite structures including columns and beams; Multi-objective optimizations (MOO) of prestressed concrete beams are performed using an ANN-based Lagrange algorithm; Offers a Pareto frontier using an ANN-based MOO for composite beams and composite columns sustaining multi-biaxial loads; Heavily illustrated in color and with diverse practical design examples in line with EC2, ACI, and ASTM codes. The book offers optimal solutions for structural designers and researchers, enabling readers to construct design charts to minimize their own design targets under various design requirements based on any design code. |
| first_indexed | 2024-09-23T23:43:26Z |
| format | software, multimedia |
| id | KOHA-OAI-TEST:611443 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-12-08T04:40:08Z |
| publishDate | 2024 |
| publisher | Boca Raton, Florida : CRC Press, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:6114432024-11-16T10:31:33ZArtificial Neural Network-based Designs of Prestressed Concrete and Composite Structures / Hong, Won‐Kee author 654926 Taylor & Francis Group (Online service) 645650 software, multimedia Electronic books 631902 Boca Raton, Florida : CRC Press,2024©2024engThis book introduces artificial neural network (ANN)-based Lagrange optimization techniques for a structural design of prestressed concrete structures based on Eurocode 2, and composite structures based on American Institute of Steel Construction and American Concrete Institute standards. The book provides robust design charts for prestressed concrete structures, which are challenging to achieve using conventional design methods. Using ANN-based design charts, the holistic design of a post-tensioned beam is performed to optimize design targets (objective functions), while calculating 21 forward outputs, in arbitrary sequences, from 21 forward inputs. Applies the powerful tools of ANN to the optimization of prestressed concrete structures and composite structures including columns and beams; Multi-objective optimizations (MOO) of prestressed concrete beams are performed using an ANN-based Lagrange algorithm; Offers a Pareto frontier using an ANN-based MOO for composite beams and composite columns sustaining multi-biaxial loads; Heavily illustrated in color and with diverse practical design examples in line with EC2, ACI, and ASTM codes. The book offers optimal solutions for structural designers and researchers, enabling readers to construct design charts to minimize their own design targets under various design requirements based on any design code.Includes bibliographical references and index.This book introduces artificial neural network (ANN)-based Lagrange optimization techniques for a structural design of prestressed concrete structures based on Eurocode 2, and composite structures based on American Institute of Steel Construction and American Concrete Institute standards. The book provides robust design charts for prestressed concrete structures, which are challenging to achieve using conventional design methods. Using ANN-based design charts, the holistic design of a post-tensioned beam is performed to optimize design targets (objective functions), while calculating 21 forward outputs, in arbitrary sequences, from 21 forward inputs. Applies the powerful tools of ANN to the optimization of prestressed concrete structures and composite structures including columns and beams; Multi-objective optimizations (MOO) of prestressed concrete beams are performed using an ANN-based Lagrange algorithm; Offers a Pareto frontier using an ANN-based MOO for composite beams and composite columns sustaining multi-biaxial loads; Heavily illustrated in color and with diverse practical design examples in line with EC2, ACI, and ASTM codes. The book offers optimal solutions for structural designers and researchers, enabling readers to construct design charts to minimize their own design targets under various design requirements based on any design code.Computer scienceEngineering & Technologyhttps://www-taylorfrancis-com.ezproxy.utm.my/books/mono/10.1201/9781003354796/artificial-neural-network-based-designs-prestressed-concrete-composite-structures-won%E2%80%90kee-hongURN:ISBN:9781003354796 |
| spellingShingle | Computer science Engineering & Technology Hong, Won‐Kee author 654926 Taylor & Francis Group (Online service) 645650 Artificial Neural Network-based Designs of Prestressed Concrete and Composite Structures / |
| title | Artificial Neural Network-based Designs of Prestressed Concrete and Composite Structures / |
| title_full | Artificial Neural Network-based Designs of Prestressed Concrete and Composite Structures / |
| title_fullStr | Artificial Neural Network-based Designs of Prestressed Concrete and Composite Structures / |
| title_full_unstemmed | Artificial Neural Network-based Designs of Prestressed Concrete and Composite Structures / |
| title_short | Artificial Neural Network-based Designs of Prestressed Concrete and Composite Structures / |
| title_sort | artificial neural network based designs of prestressed concrete and composite structures |
| topic | Computer science Engineering & Technology |
| url | https://www-taylorfrancis-com.ezproxy.utm.my/books/mono/10.1201/9781003354796/artificial-neural-network-based-designs-prestressed-concrete-composite-structures-won%E2%80%90kee-hong |
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