Finite element model updating method for long span bridges based on PSO-GRNN

被引：0

作者：

Zhou, Hongli ^{[1
]}

Zhou, Guangdong ^{[1
]}

Liu, Kaikai ^{[1
]}

Xi, Jiahuan ^{[1
]}

机构：

[1] College of Civil and Transportation Engineering, Hohai University, Nanjing

来源：

Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2024年 / 54卷 / 06期

关键词：

finite element model; generalized regression neural network; long-span bridge; model updating; particle swarm optimization algorithm;

D O I：

10.3969/j.issn.1001-0505.2024.06.018

中图分类号：

学科分类号：

摘要：

A method based on particle swarm optimization algorithm-generalized regression neural network (PSO-GRNN) was proposed for high-precision updating of the finite element model of large-span bridges. In this method, the generalized regression neural network (GRNN) was employed to describe the complex nonlinear relationship between the output of the finite element model and the parameters to be updated, and the particle swarm optimization (PSO) algorithm was adopted to optimize the smoothness factor of GRNN. The proposed updating method was verified using the finite element model of a long-span steel box girder suspension bridge. The results indicate that the GRNN optimized by PSO can more accurately describe the nonlinear relationship between frequencies and the parameters to be updated, and the prediction errors are significantly reduced. Compared with the error back propagation neural network method, the updated frequency errors of the GRNN and PSO-GRNN method are smaller. Due to the optimization of PSO, the updated frequency error of the PSO-GRNN based updating method is further reduced, and the maximum error is less than 5%. The updating method based on PSO-GRNN can be used for updating finite element models of various large-span bridges. © 2024 Southeast University. All rights reserved.

引用

页码：1489 / 1495

页数：6

共 23 条

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