Operational Modal Analysis (OMA), also called output-only modal analysis, is the identification of a structure's modal properties (natural frequencies, mode shapes, damping ratios) using only response measurements, without measuring the input excitation. It is the primary modal identification technique for full-scale structures under ambient or operational conditions.
Operational Modal Analysis (OMA) is the identification of a structure's modal properties β natural frequencies, mode shapes, and damping ratios β using only the measured response of the structure. Unlike experimental modal analysis (EMA), which requires measuring both input (excitation) and output (response), OMA operates in output-only mode: the excitation is assumed to be a broadband, stationary random process (such as ambient wind, traffic, or microtremors) that is not measured. This makes OMA particularly well-suited for large civil structures β bridges, buildings, towers, offshore platforms β where applying a controlled input is impractical or impossible.
OMA is the preferred modal identification technique for full-scale structures in service. It uses ambient excitation that is always present, requires no interruption of the structure's operation, and can be applied continuously for long-term monitoring. The measured responses are typically accelerations, though velocity or displacement measurements can also be used. The identification methods fall into two broad families. Frequency-domain methods β such as peak picking (PP), frequency domain decomposition (FDD), and enhanced FDD (EFDD) β operate on the power spectral density (PSD) matrix of the response, identifying peaks that correspond to modal frequencies and using singular value decomposition to extract mode shapes. Time-domain methods β such as stochastic subspace identification (SSI), eigensystem realization algorithm (ERA), and autoregressive moving average (ARMA) models β operate directly on the time histories and use state-space or ARMA representations to identify modal properties. Modern OMA uses automated versions of these methods (e.g., SSI-COV, SSI-DATA, automated FDD) that can process long records and continuously track modal properties over time.
OMA is central to structural health monitoring (SHM) because it enables continuous tracking of modal properties without disrupting the structure's operation. By monitoring how natural frequencies and mode shapes change over time, engineers can detect damage, track stiffness degradation, and assess the effects of environmental and operational variability. However, OMA has important limitations that must be understood: (1) it cannot separate closely spaced or highly damped modes as reliably as EMA; (2) it provides only relative mode shapes (the modal scaling is unknown); (3) it assumes broadband stationary excitation, which may not hold for structures under strong transient loading (e.g., earthquakes); and (4) it is sensitive to sensor placement, recording duration, and the number of sensors. For earthquake engineering applications, OMA is often used together with earthquake response records: the modal properties identified from ambient vibration provide a baseline against which the response to strong motion can be compared. In Iran, OMA is increasingly applied to bridge monitoring, historical structure assessment, and SHM of critical facilities, with growing interest in integrating OMA with machine learning for automated damage detection.