Guided Firework Algorithm (GFWA) Optimization Research on Viscoelastic Damper (VED) Structure Based on Vulnerability Evaluation

Guided Firework Algorithm (GFWA) Optimization Research on Viscoelastic Damper (VED) Structure Based on Vulnerability Evaluation

The vulnerability curve serves as a precise evaluation metric for structural seismic performance and a critical component in earthquake loss assessment. In this study, the orthogonal expansion method for random ground motion generation is integrated with the probability density evolution method (PDE...

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Bibliographic Details
Main Authors: Xianjie Wang, Chunyu Lei, Mengjie Xiang, Donghai Jiang, Xin Wang
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Buildings
Subjects:
vulnerability curve
random ground motion generation
PDEM
dynamic reliability
GFWA
VED parameter optimization
Online Access:https://www.mdpi.com/2075-5309/15/5/712
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Summary:The vulnerability curve serves as a precise evaluation metric for structural seismic performance and a critical component in earthquake loss assessment. In this study, the orthogonal expansion method for random ground motion generation is integrated with the probability density evolution method (PDEM) to address the dynamic reliability and vulnerability of general Multi-Degree of Freedom (MDOF) nonlinear structures. By employing dynamic reliability as a constraint and vulnerability as an evaluation index, the guided firework algorithm (GFWA) is introduced to optimize the design of viscoelastic damper (VED) structure systems. To validate the proposed methods, several examples are presented, including the generation of artificial waves, the vulnerability analysis of a five-story reinforced concrete (RC) structure, and a comparative study of GFWA and genetic algorithm (GA) optimization for VED parameters to assess the optimization efficiency. The results demonstrate that the proposed vulnerability method achieves satisfactory accuracy and is well suited for evaluating damper structure optimization designs. Furthermore, GFWA outperforms GA significantly in terms of efficiency and feasibility, offering a promising approach for optimization design in architectural structures.
ISSN:2075-5309

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