Modal identification is one of the core topics within the realm of structural health monitoring (SHM). In this study, we summarize four modal mechanical properties and propose a mechanics-informed neural network (MINN) method for structural modal identification. The proposed MINN method incorporates the sparsity of the data in the time–frequency domain and cross-correlation minimization in the time domain into the neural network to obtain modal parameters, which uses sparsity constraint and cross-correlation minimization constraint to obtain the accurate modal responses and mode shapes. Subsequently, modal frequencies and damping ratios can be derived from the modal responses. The proposed MINN method is verified by numerical simulations and two actual suspension bridges. Compared with traditional methods, the proposed MINN method has two major advantages. Firstly, the proposed MINN method presents explicit mathematical equations to distinguish the modes and the spurious modes, which obviates the necessity for priori information such as model order or time-consuming manual intervention to distinguish the modes and the spurious modes. Therefore, it can be implemented adaptively to determine the modal order and obtain the modal parameters. Secondly, the proposed MINN method can obtain a greater number of accurate modal parameters than traditional methods and achieves an increase of 102.6%, 43.4%, and 31.5% in the number of accurate results when compared to covariance-driven stochastic subspace identification (SSI-COV), data-driven stochastic subspace identification (SSI-DATA) and the natural excitation technique and the eigensystem realization algorithm (NExT-ERA), respectively. Therefore, the proposed MINN method provides an adaptively modal identification method that has clear modal mechanical properties to distinguish the modes and the spurious modes and can obtain a greater number of accurate results.

中文翻译：

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用于结构模态识别的力学通知神经网络方法


模态识别是结构健康监测（SHM）领域的核心主题之一。在本研究中，我们总结了四种模态力学特性，并提出了一种用于结构模态识别的力学信息神经网络（MINN）方法。所提出的MINN方法将时频域数据的稀疏性和时域互相关最小化融入到神经网络中以获得模态参数，利用稀疏约束和互相关最小化约束来获得准确的模态响应和振型。随后，可以从模态响应导出模态频率和阻尼比。所提出的 MINN 方法通过数值模拟和两个实际悬索桥进行了验证。与传统方法相比，所提出的 MINN 方法有两大优点。首先，所提出的 MINN 方法提出了明确的数学方程来区分模式和杂散模式，这消除了使用模型阶数等先验信息或耗时的手动干预来区分模式和杂散模式的必要性。因此，可以自适应地确定模态阶数并获得模态参数。其次，所提出的 MINN 方法比传统方法可以获得更多的准确模态参数，并且与协方差驱动的随机子空间识别（SSI- COV）、数据驱动的随机子空间识别（SSI-DATA）以及自然激励技术和特征系统实现算法（NExT-ERA）。 因此，所提出的MINN方法提供了一种自适应模态识别方法，该方法具有清晰的模态力学特性来区分模态和杂散模态，并且可以获得更多的准确结果。