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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/9/1446 |
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| Summary: | 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. |
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| ISSN: | 2075-5309 |