Bridge Structural Damage Detection Based on Covariance-Driven Stochastic Subspace Identification

With the rapid development of urban construction and infrastructure, bridges serve as critical transportation hubs, and their structural integrity is directly linked to public safety. This paper presents a bridge structural damage detection method based on Covariance-driven Stochastic Subspace Identification (Cov-SSI) and hypothesis testing. A vibration sensor network is deployed to collect bridge vibration data, providing high-quality input for subsequent modal analysis. The proposed method performs modal identification using the long-term bridge vibration data, extracts stable modal frequencies using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and ultimately applies the two-sample two-tailed t-test to determine the damage state of the bridge. This study utilizes data from the Swiss Z24 bridge for validation. Experimental results demonstrate that the proposed method effectively mitigates the influence of environmental factors, accurately identifies bridge damage in a timely manner, and provides a reliable basis for preventive maintenance. The proposed method can be applied to the long-term health monitoring of various critical infrastructures, enhancing safety assessments and maintenance decision-making.