AI-based Lagrange optimization for designing reinforced concrete columns
Structural engineers face several code-restricted design decisions. Codes impose many conditions and requirements to the designs of structural frames, such as columns and beams. However, it is difficult to intuitively find optimized solutions, while satisfying all code requirements simultaneously. E...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-11-01
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| Series: | Journal of Asian Architecture and Building Engineering |
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| Online Access: | http://dx.doi.org/10.1080/13467581.2021.1971998 |
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| _version_ | 1846166727045414912 |
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| author | Won-Kee Hong Manh Cuong Nguyen |
| author_facet | Won-Kee Hong Manh Cuong Nguyen |
| author_sort | Won-Kee Hong |
| collection | DOAJ |
| description | Structural engineers face several code-restricted design decisions. Codes impose many conditions and requirements to the designs of structural frames, such as columns and beams. However, it is difficult to intuitively find optimized solutions, while satisfying all code requirements simultaneously. Engineers commonly make design decisions based on empirical observations. Optimization techniques can be employed to make more rational engineering decisions, which result in designs that can meet various code restrictions simultaneously. Lagrange optimization techniques with constraints, not based on explicit parameterization, are implemented to make rational engineering decisions and find minimized or maximized design values by solving nonlinear optimization problems under strict constraints imposed by design codes. It is difficult to express objective functions analytically directly in terms of design variables to use derivative methods, such as Lagrange multipliers. This study proposes the use of neural network to approximate well-behaved objective functions and other output parameters into one universal function that can also give a generalizable solution for operating Jacobian and Hessian matrices to solve the Lagrangian function. The proposed method was applied successfully in optimizing a cost of a reinforced concrete column under various design requirements. An efficacy of optimal results was also proven by 5 million datasets. |
| format | Article |
| id | doaj-art-0593d92a632e418eb9e0737bd63d798a |
| institution | Kabale University |
| issn | 1347-2852 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-0593d92a632e418eb9e0737bd63d798a2024-11-15T10:36:00ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522022-11-012162330234410.1080/13467581.2021.19719981971998AI-based Lagrange optimization for designing reinforced concrete columnsWon-Kee Hong0Manh Cuong Nguyen1Kyung Hee UniversityKyung Hee UniversityStructural engineers face several code-restricted design decisions. Codes impose many conditions and requirements to the designs of structural frames, such as columns and beams. However, it is difficult to intuitively find optimized solutions, while satisfying all code requirements simultaneously. Engineers commonly make design decisions based on empirical observations. Optimization techniques can be employed to make more rational engineering decisions, which result in designs that can meet various code restrictions simultaneously. Lagrange optimization techniques with constraints, not based on explicit parameterization, are implemented to make rational engineering decisions and find minimized or maximized design values by solving nonlinear optimization problems under strict constraints imposed by design codes. It is difficult to express objective functions analytically directly in terms of design variables to use derivative methods, such as Lagrange multipliers. This study proposes the use of neural network to approximate well-behaved objective functions and other output parameters into one universal function that can also give a generalizable solution for operating Jacobian and Hessian matrices to solve the Lagrangian function. The proposed method was applied successfully in optimizing a cost of a reinforced concrete column under various design requirements. An efficacy of optimal results was also proven by 5 million datasets.http://dx.doi.org/10.1080/13467581.2021.1971998ai-based lagrange optimizationgeneralizable optimization algorithmai-based kkt conditionsann-based hessian calculus |
| spellingShingle | Won-Kee Hong Manh Cuong Nguyen AI-based Lagrange optimization for designing reinforced concrete columns Journal of Asian Architecture and Building Engineering ai-based lagrange optimization generalizable optimization algorithm ai-based kkt conditions ann-based hessian calculus |
| title | AI-based Lagrange optimization for designing reinforced concrete columns |
| title_full | AI-based Lagrange optimization for designing reinforced concrete columns |
| title_fullStr | AI-based Lagrange optimization for designing reinforced concrete columns |
| title_full_unstemmed | AI-based Lagrange optimization for designing reinforced concrete columns |
| title_short | AI-based Lagrange optimization for designing reinforced concrete columns |
| title_sort | ai based lagrange optimization for designing reinforced concrete columns |
| topic | ai-based lagrange optimization generalizable optimization algorithm ai-based kkt conditions ann-based hessian calculus |
| url | http://dx.doi.org/10.1080/13467581.2021.1971998 |
| work_keys_str_mv | AT wonkeehong aibasedlagrangeoptimizationfordesigningreinforcedconcretecolumns AT manhcuongnguyen aibasedlagrangeoptimizationfordesigningreinforcedconcretecolumns |