Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II
This study investigates two optimization strategies to enhance the seismic performance of cable-stayed bridges equipped with Fluid Viscous Dampers (FVDs). A detailed finite element model of a case study bridge was developed to evaluate the effectiveness of these strategies in optimizing FVD paramete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/9/1446 |
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| author | Qunfeng Liu Zhen Liu Jun Zhao Yuhang Lei Shimin Zhu Xing Wu |
| author_facet | Qunfeng Liu Zhen Liu Jun Zhao Yuhang Lei Shimin Zhu Xing Wu |
| author_sort | Qunfeng Liu |
| collection | DOAJ |
| description | This study investigates two optimization strategies to enhance the seismic performance of cable-stayed bridges equipped with Fluid Viscous Dampers (FVDs). A detailed finite element model of a case study bridge was developed to evaluate the effectiveness of these strategies in optimizing FVD parameters for seismic mitigation. The first strategy employs a traditional parametric analysis approach, which identifies optimal parameters by examining their influence on seismic performance. The second strategy employs a data-driven surrogate model, specifically an Artificial Neural Network (ANN), integrated with the NSGA-II optimization algorithm. This surrogate model significantly reduced computational demands during the optimization process, offering a more efficient and scalable solution for the optimization process. Results demonstrate that the ANN-based approach effectively addresses multi-objective optimization challenges while providing a robust framework for improved seismic performance in cable-stayed bridges. These findings highlight the potential of the ANN-based strategy in the seismic optimization of FVD parameters for cable-stayed bridges. |
| format | Article |
| id | doaj-art-dd286af3bd8e408a9821b810a85a84b4 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-dd286af3bd8e408a9821b810a85a84b42025-08-20T03:52:56ZengMDPI AGBuildings2075-53092025-04-01159144610.3390/buildings15091446Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-IIQunfeng Liu0Zhen Liu1Jun Zhao2Yuhang Lei3Shimin Zhu4Xing Wu5School of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Science, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCCCC First Highway Consultants Co., Ltd., Xi’an 710068, ChinaCCCC First Highway Consultants Co., Ltd., Xi’an 710068, ChinaThis study investigates two optimization strategies to enhance the seismic performance of cable-stayed bridges equipped with Fluid Viscous Dampers (FVDs). A detailed finite element model of a case study bridge was developed to evaluate the effectiveness of these strategies in optimizing FVD parameters for seismic mitigation. The first strategy employs a traditional parametric analysis approach, which identifies optimal parameters by examining their influence on seismic performance. The second strategy employs a data-driven surrogate model, specifically an Artificial Neural Network (ANN), integrated with the NSGA-II optimization algorithm. This surrogate model significantly reduced computational demands during the optimization process, offering a more efficient and scalable solution for the optimization process. Results demonstrate that the ANN-based approach effectively addresses multi-objective optimization challenges while providing a robust framework for improved seismic performance in cable-stayed bridges. These findings highlight the potential of the ANN-based strategy in the seismic optimization of FVD parameters for cable-stayed bridges.https://www.mdpi.com/2075-5309/15/9/1446cable-stayed bridgefluid viscous damperparameter optimizationsurrogate modelNSGA-II |
| spellingShingle | Qunfeng Liu Zhen Liu Jun Zhao Yuhang Lei Shimin Zhu Xing Wu Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II Buildings cable-stayed bridge fluid viscous damper parameter optimization surrogate model NSGA-II |
| title | Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II |
| title_full | Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II |
| title_fullStr | Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II |
| title_full_unstemmed | Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II |
| title_short | Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II |
| title_sort | seismic optimization of fluid viscous dampers in cable stayed bridges a case study using surrogate models and nsga ii |
| topic | cable-stayed bridge fluid viscous damper parameter optimization surrogate model NSGA-II |
| url | https://www.mdpi.com/2075-5309/15/9/1446 |
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