Recent Advances and Future Prospects of Bayesian Operational Modal Analysis: Identification Algorithms, Uncertainty Computation, and Applications

Bayesian operational modal analysis (OMA) provides a probabilistic framework for identifying modal parameters of structures under ambient excitation while quantifying identification uncertainty. By casting modal identification as a Bayesian inference problem, it enables systematic incorporation of modeling assumptions, measurement noise, and data limitations, thereby addressing fundamental shortcomings of conventional OMA methods. This paper presents a comprehensive review of Bayesian OMA, covering its theoretical foundations, representative identification algorithms, uncertainty quantification and management, and practical applications. Emphasis is placed on frequency domain Bayesian formulations, fast Bayesian FFT-based identification algorithms, treatment of multi-setup and asynchronous data, closely spaced modes, and recent advances in both computational acceleration and capturing environmental variations. Developments on uncertainty laws are synthesized to elucidate the fundamental limits of achievable identification precision and their implications for uncertainty management and test design. A range of applications is reviewed to demonstrate how Bayesian OMA methods support robust modal identification and long-term structural health monitoring under operational and environmental variations. Finally, key challenges and future research directions are discussed to facilitate further methodological development and engineering adoption of Bayesian OMA.