西南交通大学学报2026，Vol.61Issue(2)：595-603,9.DOI:10.3969/j.issn.0258-2724.20250152
改进约束子结构法及其在古建筑结构动力分析中的应用
An Improved Isolated Substructure Method and Its Application in Dynamic Analysis of an Ancient Architecture
摘要
Abstract
Obtaining the vibrational characteristics of independent substructures from global structures is crucial.The conventional isolated substructure method with time series(SIM-TS)suffers from increased computational errors due to excessively small singular values under noisy conditions.To address this,an improved SIM-TS method named ISIM-TS is proposed,aiming to achieve higher accuracy in substructure modal parameter identification.First,based on SIM-TS,an adaptive truncated singular value decomposition technique was introduced,optimizing the decomposition results by dynamically adjusting the truncation threshold.The ISIM-TS was combined with the covariance-driven stochastic subspace method(SSI-COV)to establish a new substructure modal identification framework,termed ISIM-TS-SSI-COV.Then,the feasibility of the proposed framework was verified via a classical five-degree-of-freedom(5-DOF)numerical simulation.Finally,this method was applied to identify the dynamic characteristics of a substructure in a Tibetan ancient architecture.The numerical results demonstrate that the improved method enhances the identification accuracy of the substructure,particularly reducing the identification error of the second-order frequency by 71.4％,under 1％noise.Furthermore,based on response data acquired under ambient excitation,the proposed method successfully identifies the first two natural frequencies of the substructure as 12.18 Hz and 13.31 Hz,respectively.The results provide an important data foundation for structural model updating and damage identification in the future.关键词
动力分析/约束子结构法/随机子空间法/模态分析/藏式古建筑Key words
dynamic analysis/isolated substructure method/stochastic subspace method/modal analysis/Tibetan ancient architecture分类
建筑与水利引用本文复制引用
郝晶,杨娜..改进约束子结构法及其在古建筑结构动力分析中的应用[J].西南交通大学学报,2026,61(2):595-603,9.基金项目
国家重点研发计划(2023YFF0906300) （2023YFF0906300）
国家自然科学基金项目(52478119) （52478119）
