<p>Recent advances in deep learning have significantly enhanced vibration-based damage assessment (VDA). However, the applicability of supervised deep learning models remains limited by their reliance on labeled data, requiring both normal and damaged states for training. Unsupervised VDA methods offer a promising alternative but face challenges in real-world applications, as raw vibration signals may lack sensitivity to subtle structural changes due to the large scale of civil infrastructures. In this study, we address these limitations by enhancing the effectiveness of an unsupervised approach to structural damage assessment. We propose a novel feature-extraction method that integrates time–frequency analysis with knowledge-based postprocessing, incorporating frequency–axis adjustments and colormap refinements. This postprocessing step enhances the visibility of vibration pattern differences associated with structural damage. To validate the proposed approach, we applied reconstruction-based unsupervised VDA algorithms to both simulated and experimental data. Numerical simulations across various damage scenarios, including different data configurations, damage severities, and damage locations, demonstrated that our refined unsupervised damage-detection method accurately captures structural dynamics. Further validation on a full-scale truss bridge with artificial damage confirmed the method’s effectiveness in localizing damage in real-world applications. These results highlight the potential of the proposed framework for improving the field applicability of unsupervised VDA algorithms.</p>