This article proposes a novel methodology that uses mathematical and numerical models of a structure to build a data set and determine crucial nodes that possess the highest sensitivity. Regression surfaces between the structural parameters and structural output features, represented by the natural frequencies of the structure and local transmissibility, are built using the numerical data set. A description of a possible experimental application is provided, where sensors are mounted at crucial nodes, and the natural frequencies and local transmissibility at each natural frequency are determined from the power spectral density and the power spectral density ratios of the sensor responses, respectively. An inverse iterative process is then applied to identify the structural parameters by matching the experimental features with the available parameters in the myriad numerical data set. Three examples are presented to demonstrate the feasibility and efficacy of the proposed methodology. The results reveal that the method was able to accurately identify the boundary coefficients and physical parameters of the Euler-Bernoulli beam as well as a highway bridge model with elastic foundations using only two measurement points. It is expected that the proposed method will have practical applications in the identification and analysis of restored structural systems with unknown parameters and boundary coefficients.

本文提出了一种新颖的方法，该方法使用结构的数学和数值模型来构建数据集并确定具有最高灵敏度的关键节点。使用数值数据集构建结构参数和结构输出特征之间的回归表面，以结构的固有频率和局部传递率表示。提供了可能的实验应用的描述，其中传感器安装在关键节点上，并且分别根据传感器响应的功率谱密度和功率谱密度比确定固有频率和每个固有频率的局部透射率。然后通过将实验特征与无数数值数据集中的可用参数进行匹配，应用逆迭代过程来识别结构参数。给出了三个例子来证明所提出方法的可行性和有效性。结果表明，该方法能够仅使用两个测量点即可准确识别Euler-Bernoulli梁的边界系数和物理参数，以及具有弹性基础的公路桥梁模型。期望该方法在参数和边界系数未知的恢复结构系统的识别和分析中具有实际应用价值。结果表明，该方法能够仅使用两个测量点即可准确识别Euler-Bernoulli梁的边界系数和物理参数，以及具有弹性基础的公路桥梁模型。预期该方法将在参数和边界系数未知的恢复结构系统的识别和分析中具有实际应用价值。结果表明，该方法能够仅使用两个测量点即可准确识别Euler-Bernoulli梁的边界系数和物理参数，以及具有弹性基础的公路桥梁模型。预期该方法将在参数和边界系数未知的恢复结构系统的识别和分析中具有实际应用价值。