Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate Model
To enhance the accuracy of finite element model simulation, a model updating method based on Bayesian theory is proposed, and the updating efficiency is improved by integrating improved Markov chain Monte Carlo (MCMC) algorithm and surrogate model. A radial basis function (RBF) surrogate model is co...
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Editorial Office of Journal of Shanghai Jiao Tong University
2025-08-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-8-1114.shtml |
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| _version_ | 1849222022298599424 |
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| author | MIAO Ji, DUAN Liping, LIU Jiming, LIN Siwei, ZHAO Jincheng |
| author_facet | MIAO Ji, DUAN Liping, LIU Jiming, LIN Siwei, ZHAO Jincheng |
| author_sort | MIAO Ji, DUAN Liping, LIU Jiming, LIN Siwei, ZHAO Jincheng |
| collection | DOAJ |
| description | To enhance the accuracy of finite element model simulation, a model updating method based on Bayesian theory is proposed, and the updating efficiency is improved by integrating improved Markov chain Monte Carlo (MCMC) algorithm and surrogate model. A radial basis function (RBF) surrogate model is constructed using the parameters to be updated as inputs and the finite element model modal responses as outputs. Whale optimization algorithm (WOA) is introduced into the MCMC algorithm and the uncertain parameters are updated. Finally, a numerical study on a simply supported beam and an experimental study on a three-story steel frame are conducted to verify the accuracy of the proposed method. The results show that WOA can significantly improve the stability and convergence speed of the MCMC algorithm, the updating efficiency can be improved by 13.9% at most, and the maximum frequency errors of the simply supported beam model and the three-story steel frame model updated by the WO-MH algorithm are 0.009% and 2.41%, respectively. The proposed model updating method can effectively enhance the simulation accuracy of the finite element model under both two-dimensional and eight-dimensional inputs, which provides technical reference for lean simulation and optimal design of building structures. |
| format | Article |
| id | doaj-art-5ca52deaef2f4a7b8d034d22b8b27d5e |
| institution | Kabale University |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2025-08-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-5ca52deaef2f4a7b8d034d22b8b27d5e2025-08-26T09:29:34ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672025-08-015981114112210.16183/j.cnki.jsjtu.2023.584Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate ModelMIAO Ji, DUAN Liping, LIU Jiming, LIN Siwei, ZHAO Jincheng0 1. Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai 200240, ChinaTo enhance the accuracy of finite element model simulation, a model updating method based on Bayesian theory is proposed, and the updating efficiency is improved by integrating improved Markov chain Monte Carlo (MCMC) algorithm and surrogate model. A radial basis function (RBF) surrogate model is constructed using the parameters to be updated as inputs and the finite element model modal responses as outputs. Whale optimization algorithm (WOA) is introduced into the MCMC algorithm and the uncertain parameters are updated. Finally, a numerical study on a simply supported beam and an experimental study on a three-story steel frame are conducted to verify the accuracy of the proposed method. The results show that WOA can significantly improve the stability and convergence speed of the MCMC algorithm, the updating efficiency can be improved by 13.9% at most, and the maximum frequency errors of the simply supported beam model and the three-story steel frame model updated by the WO-MH algorithm are 0.009% and 2.41%, respectively. The proposed model updating method can effectively enhance the simulation accuracy of the finite element model under both two-dimensional and eight-dimensional inputs, which provides technical reference for lean simulation and optimal design of building structures.https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-8-1114.shtmlmodel updatingbayesian theorymarkov chain monte carlo (mcmc)whale optimization algorithm (woa)surrogate model |
| spellingShingle | MIAO Ji, DUAN Liping, LIU Jiming, LIN Siwei, ZHAO Jincheng Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate Model Shanghai Jiaotong Daxue xuebao model updating bayesian theory markov chain monte carlo (mcmc) whale optimization algorithm (woa) surrogate model |
| title | Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate Model |
| title_full | Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate Model |
| title_fullStr | Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate Model |
| title_full_unstemmed | Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate Model |
| title_short | Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate Model |
| title_sort | structural simulation model updating based on improved mcmc algorithm and surrogate model |
| topic | model updating bayesian theory markov chain monte carlo (mcmc) whale optimization algorithm (woa) surrogate model |
| url | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-8-1114.shtml |
| work_keys_str_mv | AT miaojiduanlipingliujiminglinsiweizhaojincheng structuralsimulationmodelupdatingbasedonimprovedmcmcalgorithmandsurrogatemodel |