Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker Optimization Algorithm
Accurate finite element (FE) models are essential for the safety assessment of civil engineering structures. However, obtaining reliable model parameters for existing bridges remains challenging due to the inability to conduct static load tests without disrupting traffic flow. To address this, this...
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2025-08-01
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| author | Weihua Zhou Hongyin Yang Jing Hao Mengxiang Zhai Hongyou Cao Zhangjun Liu Kang Wang |
| author_facet | Weihua Zhou Hongyin Yang Jing Hao Mengxiang Zhai Hongyou Cao Zhangjun Liu Kang Wang |
| author_sort | Weihua Zhou |
| collection | DOAJ |
| description | Accurate finite element (FE) models are essential for the safety assessment of civil engineering structures. However, obtaining reliable model parameters for existing bridges remains challenging due to the inability to conduct static load tests without disrupting traffic flow. To address this, this study proposes an FE model updating framework that integrates the response surface method and the nutcracker optimization algorithm (NOA). This framework is characterized by the incorporation of ambient vibration data into parameter optimization, thereby enhancing model accuracy. The stochastic subspace identification method is first adopted to extract the bridge’s natural frequencies from vibration data. The response surface method is then employed to construct a response surface function that approximates the FE model. The NOA is subsequently applied to iteratively optimize this response surface function, ensuring rapid convergence and the precise adjustment of the FE model parameter. To validate the effectiveness of the proposed framework, a continuous beam–arch composite bridge with a span of 204.783 m was selected as a case study. The results indicate that the proposed method reduced the average frequency error from 5.58% to 2.75% by updating the model parameters. While the whale optimization algorithm required 21 iterations and the grey wolf optimizer needed 41 iterations to converge near the minimum, the NOA achieved this in merely 13 iterations, demonstrating the NOA’s superior convergence speed. Furthermore, the NOA significantly outperformed both the whale optimization algorithm and the grey wolf optimizer in reducing the error of the first transverse vibration frequency. |
| format | Article |
| id | doaj-art-48616af177bb4ad080363e5d479006a3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-48616af177bb4ad080363e5d479006a32025-08-20T03:36:26ZengMDPI AGSensors1424-82202025-08-012515483110.3390/s25154831Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker Optimization AlgorithmWeihua Zhou0Hongyin Yang1Jing Hao2Mengxiang Zhai3Hongyou Cao4Zhangjun Liu5Kang Wang6School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, ChinaAccurate finite element (FE) models are essential for the safety assessment of civil engineering structures. However, obtaining reliable model parameters for existing bridges remains challenging due to the inability to conduct static load tests without disrupting traffic flow. To address this, this study proposes an FE model updating framework that integrates the response surface method and the nutcracker optimization algorithm (NOA). This framework is characterized by the incorporation of ambient vibration data into parameter optimization, thereby enhancing model accuracy. The stochastic subspace identification method is first adopted to extract the bridge’s natural frequencies from vibration data. The response surface method is then employed to construct a response surface function that approximates the FE model. The NOA is subsequently applied to iteratively optimize this response surface function, ensuring rapid convergence and the precise adjustment of the FE model parameter. To validate the effectiveness of the proposed framework, a continuous beam–arch composite bridge with a span of 204.783 m was selected as a case study. The results indicate that the proposed method reduced the average frequency error from 5.58% to 2.75% by updating the model parameters. While the whale optimization algorithm required 21 iterations and the grey wolf optimizer needed 41 iterations to converge near the minimum, the NOA achieved this in merely 13 iterations, demonstrating the NOA’s superior convergence speed. Furthermore, the NOA significantly outperformed both the whale optimization algorithm and the grey wolf optimizer in reducing the error of the first transverse vibration frequency.https://www.mdpi.com/1424-8220/25/15/4831continuous beam–arch composite bridgefinite element model updatingresponse surface methodnutcracker optimization algorithmstochastic subspace identification |
| spellingShingle | Weihua Zhou Hongyin Yang Jing Hao Mengxiang Zhai Hongyou Cao Zhangjun Liu Kang Wang Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker Optimization Algorithm Sensors continuous beam–arch composite bridge finite element model updating response surface method nutcracker optimization algorithm stochastic subspace identification |
| title | Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker Optimization Algorithm |
| title_full | Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker Optimization Algorithm |
| title_fullStr | Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker Optimization Algorithm |
| title_full_unstemmed | Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker Optimization Algorithm |
| title_short | Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker Optimization Algorithm |
| title_sort | finite element model updating for a continuous beam arch composite bridge based on the rsm and a nutcracker optimization algorithm |
| topic | continuous beam–arch composite bridge finite element model updating response surface method nutcracker optimization algorithm stochastic subspace identification |
| url | https://www.mdpi.com/1424-8220/25/15/4831 |
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