Bridge Damping Ratio Identification and Variation Analysis Based on Two‐Year Monitoring Data Considering Operational Environment Effects

Damping ratio estimation for bridges under operational conditions typically employs operational modal analysis (OMA) methods. However, existing comparisons of these methods often overlook the nonstationary nature of traffic loads. This study focuses on two key aspects: (1) the performance evaluation of four OMA methods, autocorrelation function (ACF), stochastic subspace identification (SSI), random decrement technique (RDT), and decay response extraction (DRE), under nonstationary traffic loading, and (2) the quantification of the effects of temperature, traffic load, and wind load on structural damping ratios. An automatic modal parameter identification approach was developed to analyze two‐year monitoring data from a single‐tower cable‐stayed bridge. The practical performance of each method was assessed statistically. Finally, a method was proposed to separate the effects of temperature and traffic loading at different time scales, and a damping ratio prediction model was established. The results indicate that both SSI and ACF methods demonstrate good performance, with the ACF method exhibiting smaller variance. SSI requires careful handling of false modes, RDT has the largest variance, and the DRE method suffers from uneven temporal distribution of identification results. Temperature and traffic loading have significant effects on the damping ratios of the bridge.