Three-stage unsupervised learning approach fusing novel pseudo-label diffusion and math-physics translating for real-time structural damage detection

Unsupervised learning techniques have been asserted as pivotal approaches in vibration-based structural damage detection due to scarce labeled data, whereas unrealistic premise on sufficient data from health status and inexplicit reflection on math-physics in various algorithms degrade their applicability in practical engineering. Therefore, this study devised a three-stage unsupervised learning approach for real-time structural damage detection. It incorporates novel pseudo-label diffusion based on different damage-sensitive-features extraction strategies, and math-physics translating using adaptative fuzzy clustering algorithm optimization. Comprehensive validations upon a well-known numerical benchmark model and full-scale laboratory shaking table tests are conducted, the results of which confirm the effectiveness and superiority of the proposed method. Integrated with novel pseudo-label diffusion and explicit math-physics translating mechanisms, the sophisticated framework is expected to fully automatically discriminate unequivocal structural damage in a strictly unsupervised schema, providing an explicit interpretation avenue from non-physical clustering to end-to-end decision-making.