Long multi-span bridges represent a broad section of the civil roadway infrastructure. Despite being so common, their condition-based maintenance through vibration-based Structural Health Monitoring (SHM) has been scarcely investigated in the literature. The dynamic identification of such structures through Operational Modal Analysis (OMA) is especially challenging due to of their quasi-periodic nature and the common existence of weak inter-span coupling. Even when designed following an isostatic scheme, there always exists a certain degree of coupling between spans due to the continuity of the deck, the pavement and imperfect expansion joints. Hence, the modal poles of the spans typically appear as dense clusters with closely spaced frequencies and mode shapes with similar wavelengths, which significantly hinders the identification of physical poles through stabilization diagrams. In this light, this paper proposes a model-based machine learning approach to conduct and interpret the OMA results of partially continuous multi-span bridges. The proposed method is a hierarchical clustering approach that leverages on the analytical solution of the vertical free vibration response of multi-span girders with weak inter-span rotational coupling, allowing the estimation of the modal features of any bridge configuration ranging from simply supported to perfectly continuous conditions. Detailed parametric analyses and discussions are presented to appraise the correlation between the inter-span rotational coupling and the clustering of the modal poles of multi-span bridges, as well as the influence of damage conditions of varying severity and extension. On this basis, a model-based cut-off distance threshold for hierarchical clustering of stable poles is proposed to assist the automation of the global OMA of multi-span bridges. The developed formulation is tested in a real-world in-operation seven-spans reinforced concrete girder bridge, the Trigno V Bridge in Italy.
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