Quality Assurance Framework for Operational Modal Analysis in Structural Health Monitoring

Operational Modal Analysis (OMA) is essential for structural health monitoring (SHM) of civil infrastructure, with modal parameters—eigenfrequencies, damping ratios, and mode shapes—providing critical indicators for structural integrity assessment. However, practical application of OMA algorithms reveals significant variations in identified parameters depending on algorithmic configurations and preprocessing choices. These variations manifest as temporal discontinuities where modes abruptly appear or disappear, proliferation of modes with inconsistent physical characteristics, and variable estimates across identical conditions, undermining monitoring system reliability and highlighting the necessity for quality assurance in OMA-based SHM. This study systematically investigates sources of variation contributing to modal parameter uncertainty in OMA implementations using factorial design of experiments applied to bridge monitoring datasets. Algorithm-specific parameters, preprocessing strategies, and modeling choices are evaluated to characterize their effects on reproducibility, stability criteria performance, and physical plausibility of modal estimates. A hierarchical Bayesian approach incorporates prior information from historical monitoring data to enable probabilistic quality assessment while quantifying uncertainties from multiple sources. The findings reveal which parameter combinations effectively demonstrate stability and reproducibility for real-world bridge structures, enabling identification of robust configurations that balance computational efficiency with reliability for automated long-term monitoring. The presented framework contributes essential tools for quality assurance of OMA-based sensing systems, establishing quantitative performance baselines and acceptance criteria for modal identification under realistic operational conditions.