【摘要】 聚类分析是数据处理中常用的无监督方法，而聚类分析参数较难精准确定，限制了该方法在结构损伤识别中的应用。为解决该问题，本文提出了一种非参数贝叶斯聚类方法，结合结构模态参数开展结构损伤识别和定量分析，拓展了非参数贝叶斯模型的应用范围。所提方法采用自然激励技术处理结构实测振动数据以得到固有频率，通过非参数贝叶斯聚类方法对数据进行聚类，最终结合极大似然异方差高斯过程和贝叶斯因子对聚类结果进行损伤定量分析。通过天津永和桥实际工程案例对所提损伤识别方法的结果进行验证，结果表明，该方法能够在不提前设置聚类参数的情况下，对结构自振频率数据进行精准聚类分析，进一步对结构不同损伤状态进行识别。更多还原

【Abstract】 Clustering analysis is a commonly used unsupervised method in data processing. However, the difficulty in accurately determining clustering parameters limits the application of this method in structural damage identification. To address this issue, a non-parametric Bayesian clustering method is proposed in this study, which combines structural modal parameters for structural damage identification and quantitative analysis, thereby expanding the application range of the non-parametric Bayesian model.First, the natural excitation method is used to extract the natural frequency from the measured vibration data of the structure.Then, the non-parametric Bayesian clustering method is employed to cluster the data. Finally, maximum likelihood heteroscedastic Gaussian process regression and Bayesian factors are combined to quantitatively analyze the clustering results for damage quantitation analysis. The results of the damage identification method are verified by the actual engineering case of Yonghe Bridge in Tianjin. The results show that this method can accurately cluster the natural frequency data and identify the different damage states of the structure without the need to pre-set clustering parameters.更多还原