东南大学学报（自然科学版）2026，Vol.56Issue(8)：1117-1124,8.DOI:10.3969/j.issn.1001-0505.2026.08.003
面向桥梁数字孪生模型更新的多策略集成学习代理优化方法
Multi-strategy integrated learning-based surrogate optimization method for bridge digital twin model updating
摘要
Abstract
To improve the efficiency and accuracy of high-frequency updating of bridge digital twins based on finite element model updating,a Kriging-based clustered parallel optimization-driven surrogate updating method was proposed.First,by embedding multiple active learning functions,a clustered parallel surrogate optimization algorithm was designed,and its optimization efficiency and accuracy were analyzed using com-plex test functions.Then,an efficient updating framework for bridge digital twins was constructed.This method was applied to a metro viaduct bridge,so as to reveal its advantages and practicality in representing the variation of bridge performance with the parameters.The results show that in complex function examples,compared with the traditional optimization algorithms,the proposed algorithm can significantly reduce the computational cost of numerical simulations while maintaining high optimization accuracy.In the bridge model updating based on measured frequencies,the convergence accuracy of the proposed method is improved by one order of magnitude compared with that of the standard Kriging surrogate-based updating method,and the com-putational time is reduced by about 58%compared with that of the particle swarm optimization-based updating method.The updated twin model can accurately predict the true dynamic responses of in-service bridges.关键词
桥梁/数字孪生更新/Kriging代理模型/主动学习/并行计算Key words
bridge/updating of digital twins/Kriging surrogate model/active learning/parallel computing分类
交通工程引用本文复制引用
梁浩,郑越,吴定俊,郭蹦,李奇..面向桥梁数字孪生模型更新的多策略集成学习代理优化方法[J].东南大学学报（自然科学版）,2026,56(8):1117-1124,8.基金项目
中国国家铁路集团有限公司科技研究开发计划资助项目(K2024G006) （K2024G006）
国家自然科学基金面上资助项目(52178432) （52178432）
上海市自然科学基金面上资助项目(ZR1472500) （ZR1472500）
上海申通地铁集团有限公司科研资助项目(JS-KY25R009). （JS-KY25R009）
