An Intelligent Blind Feature Clustering Framework for Automated Modal Discrimination of Concrete Dams

Automated modal identification of concrete dams under ambient excitation remains challenging because spurious modes arising from non-white excitation, noise, and algorithmic artifacts can obscure physical modes and complicate automated discrimination. Conventional approaches, such as stabilization diagram interpretation in stochastic subspace identification, still require extensive user expertise. This study proposes an intelligent blind feature clustering framework built on a recently developed blind subspace identification method that separates modal responses without stabilization diagrams. In this framework, an improved differential evolution algorithm integrated with K-means clustering searches for the optimal clustering scheme in the feature space defined by modal feature indices extracted from the separated modal responses. Compared with conventional approaches, the proposed framework avoids subjective threshold selection and manual pole picking, thereby facilitating automated modal discrimination. The framework was applied to a large concrete arch-gravity dam. Five physical modes were identified, and the remaining modes were classified as spurious. The identified modal parameters agree well with those obtained by SSI-COV, FDD, and FEM, and the proposed clustering yields more stable results than conventional K-means with random initialization. In this case study, the proposed framework achieved automated modal discrimination without constructing or interpreting stabilization diagrams, while reducing reliance on expert intervention. These results suggest a promising approach for automated modal identification and long-term modal monitoring of concrete dams.