Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. We introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. Our results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.

基于子空间的系统识别算法已被开发为一种用于执行模态分析的先进技术。我们引入了一种新的基于张量子空间的算法来识别桥梁结构的时变模态参数。在传统的Hankel矩阵中引入了新的时间维度，建立了张量子空间分解的数学模型。结合稳定图，张量并行因子分解用于估计频率、模态振型和模态阻尼比。通过将其与经典的基于滑动窗口的随机子空间算法在模型斜拉桥动态试验中进行比较，验证了该算法的有效性。所提出的算法进一步应用于处理真实桥梁健康监测系统的动态响应，以识别其时变模态频率。我们的结果表明，所提出的算法显着减少了计算工作量，并扩展了未来时变系统识别问题的解决思路范围。