An innovative damage identification method using the nearest neighbor search method to assess 3D structures is presented. The frequency response function was employed as the input parameters to detect the severity and place of damage in 3D spaces since it includes the most dynamic characteristics of the structures. Two-dimensional principal component analysis was utilized to reduce the size of the frequency response function data. The nearest neighbor search method was employed to detect the severity and location of damage in different damage scenarios. The accuracy of the approach was verified using measured data from an experimental test; moreover, two asymmetric 3D numerical examples were considered as the numerical study. The superiority of the method was demonstrated through comparison with the results of damage identification by using artificial neural network. Different levels of white Gaussian noise were used for polluting the frequency response function data to investigate the robustness of the methods against noise-polluted data. The results indicate that both methods can efficiently detect the damage properties including its severity and location with high accuracy in the absence of noise, but the nearest neighbor search method is more robust against noisy data than the artificial neural network.

提出了一种使用最近邻搜索方法评估 3D 结构的创新损伤识别方法。频率响应函数被用作输入参数来检测 3D 空间中损坏的严重程度和位置，因为它包括结构的最动态特性。二维主成分分析被用来减少频率响应函数数据的大小。采用最近邻搜索法检测不同损伤场景下损伤的严重程度和位置。使用来自实验测试的测量数据验证了该方法的准确性；此外，两个不对称的 3D 数值例子被认为是数值研究。通过与人工神经网络损伤识别结果的对比，证明了该方法的优越性。使用不同级别的高斯白噪声污染频率响应函数数据，以研究这些方法对噪声污染数据的鲁棒性。结果表明，在没有噪声的情况下，这两种方法都可以有效地检测损坏属性，包括其严重程度和位置，但最近邻搜索方法比人工神经网络对噪声数据更鲁棒。