Ground-based interferometric radar for bridge dynamic system identification: A multi-domain operational modal analysis and integrated method selection framework

This study proposes a cohesive multi-domain analytical framework for Ground-Based Interferometric Radar (GBIR) dynamic system identification (DSI), designed to overcome inherent spatial resolution limits and range-bin averaging in non-contact bridge monitoring. While GBIR enables high-precision displacement monitoring, the extraction of reliable modal parameters under operational conditions, independent of auxiliary contact sensors, remains a significant technical bottleneck. The proposed framework addresses this by integrating seven systematically derived advanced computational methods into a multi-domain operational modal analysis (OMA) and integrated method-selection pipeline. This architecture is structured around three analytical pillars: (i) spatial-multivariate identification, (ii) frequency-domain benchmarking, and (iii) non-stationary time-frequency tracking. The framework was validated using field data from the Dong-Yi cable-stayed bridge in South Korea, and internally cross-validated by verifying agreement in modal estimates across the three analytical pillars. By utilising the systematic agreement across the three pillars as a mechanism for internal consistency and methodological reliability, the methodology demonstrates that multi-domain approaches, specifically Covariance-driven Stochastic Subspace Identification (Cov-SSI) and Enhanced Frequency Domain Decomposition (EFDD), exploit spatial correlations across radar range-bins to achieve superior modal separation. Results show that the framework successfully isolates three dominant natural frequencies (0.33 Hz, 0.46 Hz, and 0.63 Hz) and resolves significant frequency discrepancies associated with univariate processing. This study demonstrates that an integrated, multi-domain analytical strategy provides the necessary methodological rigour to establish GBIR as a viable independent solution for the dynamic characterisation and long-term health assessment of complex bridge infrastructure. • Dynamic identification of bridges using non-contact GBIR sensors • Integrated multi-domain OMA framework for robust structural identification • Method selection pipeline for time, frequency, and time-frequency analysis • Mode-shape recovery using Cov-SSI with spatial ordering analysis • Enhanced visualization of structural modes and frequency ridges