Support Vector Regression-Based Optimization for Triple Friction Pendulum Bearings: Achieving Computational Efficiency
Triple friction pendulum bearings (TFPBs) enhance the seismic resilience of base-isolated buildings. However, optimizing TFPB designs is computationally expensive, necessitating surrogate modeling techniques. This study utilizes structural modeling through OpenSees, while the optimization process, e...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Pouyan Press
2026-01-01
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| Series: | Journal of Soft Computing in Civil Engineering |
| Subjects: | |
| Online Access: | https://www.jsoftcivil.com/article_223690_b03f1bcfa3e6a04cfd640c5895ae4009.pdf |
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| Summary: | Triple friction pendulum bearings (TFPBs) enhance the seismic resilience of base-isolated buildings. However, optimizing TFPB designs is computationally expensive, necessitating surrogate modeling techniques. This study utilizes structural modeling through OpenSees, while the optimization process, employing the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), is conducted in MATLAB for the design of TFPB systems. The primary objective is to minimize peak floor acceleration and inter-story drift. A Support Vector Regression (SVR) surrogate simulates the seismic response of models subjected to synthetic earthquake records tailored to site-specific hazards. To closely investigate the SVR method, three kernel functions (Gaussian, Matérn 3/2, and Matérn 5/2) were compared. SVR surrogate modeling reduced computational costs by 77.78%, with prediction differences of less than 2.71% for both objectives, demonstrating high fidelity. Additionally, no single kernel function consistently outperforms others across all optimization problems within the SVR method. In this study, the Gaussian kernel yielded superior results for the peak floor acceleration objective, while the Matérn 5/2 kernel provided better performance for the inter-story drift objective. This indicates that the choice of kernel function may need to be tailored to specific optimization goals. |
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| ISSN: | 2588-2872 |