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作 者:简嘉伟 汪利 吕中荣 JIAN Jia-wei;WANG Li;LYU Zhong-rong(School of Aeronautics and Astronautics,Sun Yat-sen University,Shenzhen,Guangdong 518107,China)
机构地区:[1]中山大学航空航天学院,广东,深圳518107
出 处:《工程力学》2025年第4期150-158,共9页Engineering Mechanics
基 金:国家自然科学基金项目(11972380)。
摘 要:模态参数是结构的重要特征参数,是结构设计、健康监测等的重要依据。针对环境激励下的工程结构,提出了一种基于协方差回归的模态参数识别方法。在环境激励和测量噪音的平稳性假设下,研究发现结构在不同时间间隔下的协方差应通过一系列标量系数呈现出线性相关性,且这些标量系数与结构的模态参数直接相关。基于此,可以先通过线性回归得到使不同时间间隔下的协方差线性相关的标量系数,然后直接从系数中提取系统的模态参数。该方法避免了传统协方差驱动算法对庞大的Hankel矩阵的分解,并对模型维度要求较小,因此在计算效率上优于一些传统的协方差驱动算法。一个剪切层模型数值算例和一座人行天桥的现场实测验证了所提方法的有效性和效率。Modal parameters are important information of a structure as well as significant basis for structural design and health monitoring.Thus,a modal parameter identification method based on covariance regression is proposed for engineering structures subjected to environmental excitations.Based on the stationary assumption of the ambient excitations and the measurement noise,it is found that the covariances at different time lags should be linearly dependent through a set of scalar coefficients that are directly related to modal parameters of the system.Consequently,the scalar coefficients associated with covariances under different time lags can be obtained firstly through linear covariance regression operation,and then the modal parameters are extracted directly from the coefficients.This method avoids the decomposition of a large Hankel matrix as done by some traditional covariance-driven algorithms and requires a smaller model dimension,which results in higher efficiency than some traditional covariance-driven algorithms.A numerical example of a shear model and a field case of a pedestrian bridge were conducted to verify the effectiveness and efficiency of the proposed covariance regression method.The results demonstrate that the modal parameters identified by the proposed method correspond with those identified by the covariance-driven stochastic subspace identification(SSI-Cov)but in a more efficient manner.
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