Structural optimization of single-layer domes using surrogate-based physics-informed neural networks
This study aims at generation of a novel artificial bee colony algorithm using surrogate finite element method with neural network technique. In this paper, theory of surrogate finite element method with physics-informed neural networks (PINNs) are generated and applied to deal with the geometricall...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023080751 |
| _version_ | 1797646451380060160 |
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| author | Hongyu Wu Yu-Ching Wu Peng Zhi Xiao Wu Tao Zhu |
| author_facet | Hongyu Wu Yu-Ching Wu Peng Zhi Xiao Wu Tao Zhu |
| author_sort | Hongyu Wu |
| collection | DOAJ |
| description | This study aims at generation of a novel artificial bee colony algorithm using surrogate finite element method with neural network technique. In this paper, theory of surrogate finite element method with physics-informed neural networks (PINNs) are generated and applied to deal with the geometrically nonlinear optimization problem of size, shape and topology for single-layer domes. In the artificial bee colony algorithm, the feedforward neural network is used to surrogate finite element analyses. Three numerical examples of 10-bar truss, Lamella dome, and Kiewit dome are carried out to verify feasibility and accuracy of the proposed method. Results of the present study are in good agreement with ones from literature. It is indicated that optimization processes can be considerably accelerated using the modified algorithm. That is, using the neural network surrogate-based models could significantly increase computational efficiency of structural optimum design for single-layer domes. |
| first_indexed | 2024-03-11T15:01:47Z |
| format | Article |
| id | doaj.art-6e3701c168be41b8bc6199fefe7bb850 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T15:01:47Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-6e3701c168be41b8bc6199fefe7bb8502023-10-30T06:07:43ZengElsevierHeliyon2405-84402023-10-01910e20867Structural optimization of single-layer domes using surrogate-based physics-informed neural networksHongyu Wu0Yu-Ching Wu1Peng Zhi2Xiao Wu3Tao Zhu4Department of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai, ChinaDepartment of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai, China; Corresponding authorDepartment of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai, ChinaDepartment of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai, ChinaDepartment of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai, ChinaThis study aims at generation of a novel artificial bee colony algorithm using surrogate finite element method with neural network technique. In this paper, theory of surrogate finite element method with physics-informed neural networks (PINNs) are generated and applied to deal with the geometrically nonlinear optimization problem of size, shape and topology for single-layer domes. In the artificial bee colony algorithm, the feedforward neural network is used to surrogate finite element analyses. Three numerical examples of 10-bar truss, Lamella dome, and Kiewit dome are carried out to verify feasibility and accuracy of the proposed method. Results of the present study are in good agreement with ones from literature. It is indicated that optimization processes can be considerably accelerated using the modified algorithm. That is, using the neural network surrogate-based models could significantly increase computational efficiency of structural optimum design for single-layer domes.http://www.sciencedirect.com/science/article/pii/S2405844023080751Structural optimizationmetaheuristicSingle-layer domesartificial bee colony algorithmneural network surrogate-based model |
| spellingShingle | Hongyu Wu Yu-Ching Wu Peng Zhi Xiao Wu Tao Zhu Structural optimization of single-layer domes using surrogate-based physics-informed neural networks Heliyon Structural optimization metaheuristic Single-layer domes artificial bee colony algorithm neural network surrogate-based model |
| title | Structural optimization of single-layer domes using surrogate-based physics-informed neural networks |
| title_full | Structural optimization of single-layer domes using surrogate-based physics-informed neural networks |
| title_fullStr | Structural optimization of single-layer domes using surrogate-based physics-informed neural networks |
| title_full_unstemmed | Structural optimization of single-layer domes using surrogate-based physics-informed neural networks |
| title_short | Structural optimization of single-layer domes using surrogate-based physics-informed neural networks |
| title_sort | structural optimization of single layer domes using surrogate based physics informed neural networks |
| topic | Structural optimization metaheuristic Single-layer domes artificial bee colony algorithm neural network surrogate-based model |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023080751 |
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