基于不同响应分解原则的ASCE结构模态参数识别对比研究  

Comparative study on modal parameter identification for ASCE Benchmark structure based on different response decomposition principles

作　　者：张志钰 刘琪钿 陈泽鹏 ZHANG Zhiyu;LIU Qitian;CHEN Zepeng(School of Transportation and Civil Engineering,Foshan University,Foshan 528225,China)

机构地区：[1]佛山大学土木与交通学院,广东佛山528225

出　　处：《佛山科学技术学院学报(自然科学版)》2025年第1期79-84,共6页Journal of Foshan University(Natural Science Edition)

基　　金：国家自然科学青年基金项目(52008109)。

摘　　要：结构模态参数是结构状态评估的重要组成部分,而不同的响应分解原则决定了模态参数识别的精度。针对ASCE Benchmark框架结构,采用不同响应分解原则进行加速度响应分解,对比了基于负熵最大化的快速独立成分分析法(FastICA)、基于峭度最大化的独立成分分析法(KrICA)和基于方差最大化的主成分分析法(PCA)等原则下的模态参数识别精度差异,为实际工程应用提供理论参考。研究结果表明:FastICA使用负熵度量非高斯性,通过牛顿迭代法使得分离信号最大程度互相独立,相比于另外两种方法具有最好的模态参数识别精度。Structural modal parameters identification is an important component of structural condition assessment,and their accuracy heavily relies on a suitable selection of response decomposition principle.In this paper,different response decomposition principles were applied for acceleration response decomposition of the ASCE Benchmark frame structure,and their modal parameter identification accuracy were compared to each other to provide a theoretical guidance in real application.Specifically,the fast independent component analysis(FastICA)based on the maximization of negentropy,the independent component analysis(ICA)based on the maximization of kurtosis,and the principal component analysis(PCA)based on the maximization of variance were utilized.FastICA measures non-Gaussianity using negentropy and employs the Newton iteration method to maximize the independence of the separated signals.The results showed that FastICA had the best accuracy in modal parameter identification compared to the other two methods.

关 键 词：快速独立成分分析 模态参数识别 ASCE Benchmark框架结构 负熵 主成分分析 

分 类 号：O327[理学—一般力学与力学基础] TU311[理学—力学]