Design Optimization of Curved Curtain Wall Based on SPEA II-GA-BPNN Algorithm
The optimization of the design of the curved curtain wall to reduce the construction cost can bring about huge economic benefits to the curtain wall project. This paper breaks through the current practice of cost optimization only in the detailed design stage. Based on the characteristics of optimiz...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2548647 |
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| _version_ | 1832553674954506240 |
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| author | YiQuan Zou HaoZhou Huang XuYong Xia Xin Wang |
| author_facet | YiQuan Zou HaoZhou Huang XuYong Xia Xin Wang |
| author_sort | YiQuan Zou |
| collection | DOAJ |
| description | The optimization of the design of the curved curtain wall to reduce the construction cost can bring about huge economic benefits to the curtain wall project. This paper breaks through the current practice of cost optimization only in the detailed design stage. Based on the characteristics of optimization objectives in different stages, a composite scheme process is proposed, which uses the combination of SPEA II multiobjective algorithm and GA single-objective algorithm for design optimization. It predicts and evaluates the optimization scheme by BP neural network. The panel meshing scheme is first optimized in the scheme design stage using the SPEA II multiobjective algorithm. The panel meshing scheme is first optimized in the scheme design stage using the SPEA II multiobjective algorithm. The best optimization results are then screened using the GA single-objective algorithm to optimize the panel type in the detailed design stage. Afterwards, BP neural network training samples are randomly generated in all the optimization schemes, and the BP neural network is trained. The trained BP neural network model is used to predict and select the optimal solution in the solution set. Finally, the initial solution, existing optimization solution, and compound optimization solution are compared and analyzed. The obtained results show that the proposed composite optimization scheme can effectively reduce the cost of panel and keel materials by almost 10%. It can provide new ideas and methods for the design of curved curtain walls. |
| format | Article |
| id | doaj-art-447c60d00ef64a8282241c9ccc6731d9 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-447c60d00ef64a8282241c9ccc6731d92025-02-03T05:53:33ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/2548647Design Optimization of Curved Curtain Wall Based on SPEA II-GA-BPNN AlgorithmYiQuan Zou0HaoZhou Huang1XuYong Xia2Xin Wang3School of Civil EngineeringSchool of Civil EngineeringBeijing Glory PKPM Technology Co., Ltd.School of Civil EngineeringThe optimization of the design of the curved curtain wall to reduce the construction cost can bring about huge economic benefits to the curtain wall project. This paper breaks through the current practice of cost optimization only in the detailed design stage. Based on the characteristics of optimization objectives in different stages, a composite scheme process is proposed, which uses the combination of SPEA II multiobjective algorithm and GA single-objective algorithm for design optimization. It predicts and evaluates the optimization scheme by BP neural network. The panel meshing scheme is first optimized in the scheme design stage using the SPEA II multiobjective algorithm. The panel meshing scheme is first optimized in the scheme design stage using the SPEA II multiobjective algorithm. The best optimization results are then screened using the GA single-objective algorithm to optimize the panel type in the detailed design stage. Afterwards, BP neural network training samples are randomly generated in all the optimization schemes, and the BP neural network is trained. The trained BP neural network model is used to predict and select the optimal solution in the solution set. Finally, the initial solution, existing optimization solution, and compound optimization solution are compared and analyzed. The obtained results show that the proposed composite optimization scheme can effectively reduce the cost of panel and keel materials by almost 10%. It can provide new ideas and methods for the design of curved curtain walls.http://dx.doi.org/10.1155/2022/2548647 |
| spellingShingle | YiQuan Zou HaoZhou Huang XuYong Xia Xin Wang Design Optimization of Curved Curtain Wall Based on SPEA II-GA-BPNN Algorithm Advances in Civil Engineering |
| title | Design Optimization of Curved Curtain Wall Based on SPEA II-GA-BPNN Algorithm |
| title_full | Design Optimization of Curved Curtain Wall Based on SPEA II-GA-BPNN Algorithm |
| title_fullStr | Design Optimization of Curved Curtain Wall Based on SPEA II-GA-BPNN Algorithm |
| title_full_unstemmed | Design Optimization of Curved Curtain Wall Based on SPEA II-GA-BPNN Algorithm |
| title_short | Design Optimization of Curved Curtain Wall Based on SPEA II-GA-BPNN Algorithm |
| title_sort | design optimization of curved curtain wall based on spea ii ga bpnn algorithm |
| url | http://dx.doi.org/10.1155/2022/2548647 |
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