###### Abstract  
An automated operational modal analysis (AOMA) method that considers the uncertainty in modal parameters is presented and data acquired from actual bridges are used to validate it. The proposed method processes stepwise, from SSI to pre-cleaning, clustering and the removal of outliers. The stochastic subspace identification (SSI) step also calculates the uncertainty of the modal parameters. In this step, the MAC (modal assurance criterion) index and its variability are additionally calculated by exploiting the alteration of the mode shapes. The pre-cleaning stage sorts out the spurious modes by means of the frequency, the coefficient of variation related to the frequency and the damping ratio, as well as the MAC index and its standard deviation. Under the assumption of normal distributions for the frequency and the MAC index, the clustering stage constructs clusters of identical modes with reference to the uncertainty of each mode. The outliers that may be contained in each of these clusters are then removed based upon the frequency, the MAC index and the damping ratio. Values for the parameters that make the proposed method applicable are suggested and are applied unilaterally to three instrumented bridges of different types. The results show that the proposed AOMA method provides accurate mode identification regardless of the bridge type. structural health monitoring; automated operational modal analysis; stochastic subspace identification; modal uncertainty; modal parameter extraction +
Footnote †: journal: _sciences_  
actual modes. In SSI, spurious modes must be filtered from the results and the actual modes must be bound in sets of identical modes. In FDD, the actual modes must be extracted from the result curves by peak picking. The whole process without the involvement of an expert and using data measured under operational conditions is called automated operational modal analysis (AOMA). [PERSON] et al. [5] proposed a method for identifying the modal domain within predefined limits using a modal coherence function and a modal domain function. However, the method provided different results according to the selection criteria adopted for the peak picking of the results obtained by FDD. A similar method was presented by [PERSON] et al. [6] in which sensitivity analysis was applied to the MAC (Modal Assurance Criterion; [PERSON] [7]) rejection level, but the approach was time-consuming in deciding the values for the diverse parameters and the results based upon the so-decided parameters lacked consistency. Hence, the performance of AOMA based on methods using FDD will depend on the values of the various parameters, including the peak picking criterion. [PERSON] et al. [8] employed a series of deterministic and probabilistic criteria as well as a fuzzy clustering approach to obtain the real modes from SSI results. [PERSON] and [PERSON] [9] monitored an actual bridge over the course of one year by applying very simple criteria for selecting only the poles representing more than five modes. [PERSON] et al. [10] proposed a very fast algorithm to obtain the real poles through the application of graph theory to the stabilization diagram resulting from a multipatch subspace approach, but with slightly degraded reliability. [PERSON], et al. [11] identified close modes by applying several parameters in a covariance-based SSI (SSI-COV) method, but the approach demanded a large number of calibrations for the computation of the parameters' values. Here also, the performance of AOMA based on methods using SSI will depend on the criteria chosen for the removal of spurious modes and the removal of outliers. Moreover, it is not guaranteed that criteria that show remarkable performance for a specific bridge will provide equivalent performance when applied to another bridge. A method making it possible to consider this matter is thus necessary. [PERSON] et al.