Identification of nonlinear structural parameters based on particle filtering algorithm
Structures such as large-span flexible bridges and high-rise buildings exhibit significant nonlinearity due to their flexibility. Therefore, a nonlinear identification method is essential for structural parameter identification in these cases. Based on the strong nonlinear Bouc-Wen model, the partic...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025018523 |
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| author | Dandan Xia Wanghua Yu Li Lin |
| author_facet | Dandan Xia Wanghua Yu Li Lin |
| author_sort | Dandan Xia |
| collection | DOAJ |
| description | Structures such as large-span flexible bridges and high-rise buildings exhibit significant nonlinearity due to their flexibility. Therefore, a nonlinear identification method is essential for structural parameter identification in these cases. Based on the strong nonlinear Bouc-Wen model, the particle filtering (PF) method is employed to identify the dynamic response and structural parameters of nonlinear structures under seismic loading. To show the performance of parameters identification, the PF method is compared with Unscented Kalman Filter method as well. The results show that the particle filtering method effectively identifies the strong nonlinear model with multiple degrees of freedom with partial measurements. As the number of particles increases, the identification error decreases; however, this also leads to increase of simulation time. Considering both the accuracy of identification results and the efficiency of the process, the best performance is achieved when N = 150 in this simulation. Based on response data from verified numerical simulation by computational fluid dynamics (CFD) method, results show that the PF method can be applied on fluid-structure vibration systems. This study provides a reference for the application of particle filtering in the health monitoring of large nonlinear structures. |
| format | Article |
| id | doaj-art-2110db04d6db4b53be782a24a67ed8c7 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-2110db04d6db4b53be782a24a67ed8c72025-08-20T02:10:07ZengElsevierResults in Engineering2590-12302025-09-012710578110.1016/j.rineng.2025.105781Identification of nonlinear structural parameters based on particle filtering algorithmDandan Xia0Wanghua Yu1Li Lin2School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, ChinaStructures such as large-span flexible bridges and high-rise buildings exhibit significant nonlinearity due to their flexibility. Therefore, a nonlinear identification method is essential for structural parameter identification in these cases. Based on the strong nonlinear Bouc-Wen model, the particle filtering (PF) method is employed to identify the dynamic response and structural parameters of nonlinear structures under seismic loading. To show the performance of parameters identification, the PF method is compared with Unscented Kalman Filter method as well. The results show that the particle filtering method effectively identifies the strong nonlinear model with multiple degrees of freedom with partial measurements. As the number of particles increases, the identification error decreases; however, this also leads to increase of simulation time. Considering both the accuracy of identification results and the efficiency of the process, the best performance is achieved when N = 150 in this simulation. Based on response data from verified numerical simulation by computational fluid dynamics (CFD) method, results show that the PF method can be applied on fluid-structure vibration systems. This study provides a reference for the application of particle filtering in the health monitoring of large nonlinear structures.http://www.sciencedirect.com/science/article/pii/S2590123025018523Particle filteringNonlinear structureParameter identificationHysteresis model |
| spellingShingle | Dandan Xia Wanghua Yu Li Lin Identification of nonlinear structural parameters based on particle filtering algorithm Results in Engineering Particle filtering Nonlinear structure Parameter identification Hysteresis model |
| title | Identification of nonlinear structural parameters based on particle filtering algorithm |
| title_full | Identification of nonlinear structural parameters based on particle filtering algorithm |
| title_fullStr | Identification of nonlinear structural parameters based on particle filtering algorithm |
| title_full_unstemmed | Identification of nonlinear structural parameters based on particle filtering algorithm |
| title_short | Identification of nonlinear structural parameters based on particle filtering algorithm |
| title_sort | identification of nonlinear structural parameters based on particle filtering algorithm |
| topic | Particle filtering Nonlinear structure Parameter identification Hysteresis model |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025018523 |
| work_keys_str_mv | AT dandanxia identificationofnonlinearstructuralparametersbasedonparticlefilteringalgorithm AT wanghuayu identificationofnonlinearstructuralparametersbasedonparticlefilteringalgorithm AT lilin identificationofnonlinearstructuralparametersbasedonparticlefilteringalgorithm |