Dynamic characteristics extracted from ambient and forced vibration tests are always associated with some level of uncertainties because of unknown nature of applied forces, existence of ambient noises as well as measurement errors. Stochastic Subspace Methods are among the most accurate and consistent methods within the domain of operational modal analysis. In this research, a new technique for Operational Modal Analysis is proposed using Stochastic Realization Theory and Canonical Correlation Analysis, which in comparison with previous methodologies, identification process are directly performed in the prediction space by extracting orthonormal vectors of data space. Considering its optimized nature, the proposed method is expected to have superior accuracy in terms of elimination of unstable poles as well as low time consumption analysis. To indicate the efficiency and accuracy of the proposed algorithm, it is applied to reanalyze the results of the forced vibration tests performed on the Shahid-Rajaee arch dam in northern Iran. These tests were conducted via steady-state sinusoidal stimulation method. More accurate natural frequencies are obtained compared to those of previously reported results, besides the fact that the first three modes of the structure were identified by the new approach, while they were not observed via the previous one. In order to examine the capabilities of the proposed method for processing of ambient vibration records, the dynamic characteristics of the Pacoima dam was identified using the recorded responses during 2001 earthquake in San Fernando, California. The results indicated good accuracy in the obtained frequencies and damping ratios compared to those obtained via data driven subspace method. Time consumption of identification process were reduced significantly (up to 50%) for both case studies indicating a faster convergence rate provided by the proposed method.

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Uncertainty in modal characteristics due to output-only system identification methods has been a challenge in operational modal analysis. The present study aims to extract modal parameters of Karun IV Dam (the highest arch dam in Iran) using the balanced stochastic subspace identification (B-SSI) and investigate the influence of user-defined parameters (i.e., columns and block rows of Hankel Matrix) on the uncertainty of the results. The effects of noise caused by numerical instabilities were first filtered using the inverse process by the condition number. Subsequently, the modal properties were homogenized with spatial clustering of applications with noise (DBSCAN) to remove the outlier and spurious characteristics. Then, the physical modes were validated by inspecting the complexity of the mode shapes based on the mode complexity factor criterion. Finally, the coefficient of variation (CV) of the validated clusters was employed to conduct a sensitivity analysis performed concerning the dimensions of the Hankel matrix to find the optimal models (with the minimum error in estimating the modal characteristics). The results indicated that the proposed method prevented the emergence of computational and noisy modes by regulating the extracted models, such that the first model of the structure was extracted with an error of less than 10% compared to the numerical model.

Based on the linearization of the structure’s vibration equation in the state space, the stochastic subspace (SSI) approach is often used for system identification in the time domain of structures. As a consequence of using singular value decomposition (SVD) and QR factorization, the non-linear optimization solution may be avoided, and the identification issue can be solved as a linear least-squares problem. Although SSI does not explicitly minimize a cost function to produce the system matrices, the statistical analysis is significantly more involved for subspace approaches. Alternatively, in system identification, one might choose an output-error method (OEM), whereby the model parameters are repeatedly tweaked to match the outputs of the simulated model and the observed system. The purpose of this study is to modify the OEM to obtain structural features in the following manner: First, to reduce the number of optimization iterations, the initial term is derived using the SSI. Second, the objective function’s nonlinearity is reduced by considering the second-order derivatives as a linear system to optimize parameters using the Gauss-Newton approach. Finally, perform a gradient project minimization in state-space systems to prevent non-injectivity. After applying OEM to the results of a model of a three-story structure activated by seismic acceleration at SNR=1dB, the model’s damping ratio and mode shapes became more precise.

Based on the linearization of the structure's vibration equation in the state space, the stochastic subspace (SSI) approach is often used for system identification in the time domain of structures. As a consequence of using singular value decomposition (SVD) and QR factorization, the non-linear optimization solution may be avoided, and the identification issue can be solved as a linear least-squares problem. Although SSI does not explicitly minimize a cost function to produce the system matrices, the statistical analysis is significantly more involved for subspace approaches. Alternatively, in system identification, one might choose an output-error method (OEM), whereby the model parameters are repeatedly tweaked to match the outputs of the simulated model and the observed system. The purpose of this study is to modify the OEM to obtain structural features in the following manner: First, to reduce the number of optimization iterations, the initial term is derived using the SSI. Second, the objective function's nonlinearity is reduced by considering the second-order derivatives as a linear system to optimize parameters using the Gauss-Newton approach. Finally, perform a gradient project minimization in state-space systems to prevent non-injectivity. After applying OEM to the results of a model of a three-story structure activated by seismic acceleration at SNR = 1dB, the model's damping ratio and mode shapes became more precise.