Structural Health Monitoring (SHM) has always been a hot topic in the field of Mechanical and civil engineering, and its first-level task is damage detection. Traditional Stochastic Subspace Identification (SSI) performs damage detection through operational modal analysis (OMA), which is a data-driven method and has high identification accuracy, but huge amount of data. Advances in artificial intelligence tools represent the frontier of SHM, enabling non-destructive assessments directly based on the output-only vibration signals. In this thesis, three methods are proposed to deal with multi-class damage classification tasks, the first method relies only on statistical features, in contrast, the second method considers the most informative subspace-based damage indicator.