An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN)
Design optimizations of round reinforced concrete columns based on artificial neural networks (ANNs) have been investigated in previous studies with only one pair of axial load (${P_u}$) and bending moment (${M_u}$). In this study, ANNs are generalized to be applicable to multiple load pairs by resh...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-09-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2023.2257287 |
| _version_ | 1797661121002340352 |
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| author | Won-Kee Hong Thuc Anh Le Manh Cuong Nguyen |
| author_facet | Won-Kee Hong Thuc Anh Le Manh Cuong Nguyen |
| author_sort | Won-Kee Hong |
| collection | DOAJ |
| description | Design optimizations of round reinforced concrete columns based on artificial neural networks (ANNs) have been investigated in previous studies with only one pair of axial load (${P_u}$) and bending moment (${M_u}$). In this study, ANNs are generalized to be applicable to multiple load pairs by reshaping weight matrices of ANNs to prevent retraining of ANNs on the large datasets. Generalized ANN-based Lagrange optimizations are proposed for structural designs of round reinforced concrete columns with multiple load combinations. The present study modularizes the weight matrix of ANNs which considers one load pair to completely capture multiple factored loads. An optimal design by ANNs based on the modularized weight matrix and Lagrange optimization techniques using the Karush–Kuhn–Tucker (KKT) conditions was performed and validated with large datasets. Design examples performed by an ANN-based method and structural mechanics demonstrate accuracies of safety factors (SF) as small as 1% − 2%, which confirms the applicability of the proposed ANNs. Based on the present study, ANNs with modularized weight matrices aid engineers in optimizing round reinforced concrete columns subject to multiple loads. |
| first_indexed | 2024-03-11T18:39:54Z |
| format | Article |
| id | doaj.art-79ee58586a0642159601f54e58cdd57e |
| institution | Directory Open Access Journal |
| issn | 1347-2852 |
| language | English |
| last_indexed | 2024-03-11T18:39:54Z |
| publishDate | 2023-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj.art-79ee58586a0642159601f54e58cdd57e2023-10-12T13:36:25ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522023-09-010011610.1080/13467581.2023.22572872257287An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN)Won-Kee Hong0Thuc Anh Le1Manh Cuong Nguyen2Kyung Hee UniversityKyung Hee UniversityKyung Hee UniversityDesign optimizations of round reinforced concrete columns based on artificial neural networks (ANNs) have been investigated in previous studies with only one pair of axial load (${P_u}$) and bending moment (${M_u}$). In this study, ANNs are generalized to be applicable to multiple load pairs by reshaping weight matrices of ANNs to prevent retraining of ANNs on the large datasets. Generalized ANN-based Lagrange optimizations are proposed for structural designs of round reinforced concrete columns with multiple load combinations. The present study modularizes the weight matrix of ANNs which considers one load pair to completely capture multiple factored loads. An optimal design by ANNs based on the modularized weight matrix and Lagrange optimization techniques using the Karush–Kuhn–Tucker (KKT) conditions was performed and validated with large datasets. Design examples performed by an ANN-based method and structural mechanics demonstrate accuracies of safety factors (SF) as small as 1% − 2%, which confirms the applicability of the proposed ANNs. Based on the present study, ANNs with modularized weight matrices aid engineers in optimizing round reinforced concrete columns subject to multiple loads.http://dx.doi.org/10.1080/13467581.2023.2257287artificial neural networksmodularized weight matricesreinforced concrete columnsmultiple load combinationslagrange with karush-kuhn-tucker conditions |
| spellingShingle | Won-Kee Hong Thuc Anh Le Manh Cuong Nguyen An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN) Journal of Asian Architecture and Building Engineering artificial neural networks modularized weight matrices reinforced concrete columns multiple load combinations lagrange with karush-kuhn-tucker conditions |
| title | An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN) |
| title_full | An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN) |
| title_fullStr | An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN) |
| title_full_unstemmed | An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN) |
| title_short | An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN) |
| title_sort | optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network ann |
| topic | artificial neural networks modularized weight matrices reinforced concrete columns multiple load combinations lagrange with karush-kuhn-tucker conditions |
| url | http://dx.doi.org/10.1080/13467581.2023.2257287 |
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