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Long-span bridges are susceptible to wind-induced vibration due to their high flexibility, low-frequency dominance, and light damping capacity. Vortex-induced vibrations (VIVs), which usually occur under in-service conditions, can result in discomfort to users and detrimental effects on the fatigue capacity of structural elements; therefore, accurate VIV assessments are essential in ensuring the vibrational serviceability of bridges. Despite the research efforts of data-driven VIV prediction, the robustness and general applicability of the proposed methods remains challenging, in that each method requires different conditions for the datasets in order to develop machine-learning (ML) models. Furthermore, collecting sufficient VIV datasets (anomaly state) from various operational conditions is impractical, time-consuming, and even impossible in some situations compared with non-VIV datasets (normal state). This …