Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the stochastic subspace identification (SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

模态参数，包括基频，阻尼比和振型，可用于评估结构的健康状况。自动模态参数识别在实时结构健康监测中起着至关重要的作用，近年来已成为热门话题。在这项研究中，提出了一种用于高拱坝的模态参数自动识别程序。通过将基于密度的应用程序空间聚类与噪声（DBSCAN）算法和随机子空间识别（SSI）相结合，可以实现所提出的过程。以210米高的大港山水坝为例，验证了该方法的可行性。结果表明，DBSCAN算法具有足够的鲁棒性，可以根据SSI解释稳定图，并且可以避免使用轮廓模式。这导致所建议的过程获得比分区聚类和分层聚类算法更好的性能。此外，拱坝识别频率的误差在4％以内，识别的模态形状与从有限元模型获得的模态一致，这表明所提出的方法足够准确，可用于模态参数识别。 。该程序对拱坝的在线模态参数识别和模态跟踪是可行的。这意味着所提出的程序足够准确，可以用于模态参数识别。该程序对拱坝的在线模态参数识别和模态跟踪是可行的。这意味着所提出的程序足够准确，可以用于模态参数识别。该程序对拱坝的在线模态参数识别和模态跟踪是可行的。