Engineering performance of two analytical methodologies for estimating modal parameter uncertainty for structures

Abstract

Stochastic subspace identification (SSI) and Bayesian methods are now the main representative approaches for high-performance system identification and uncertainty quantification. Both methods have been extensively used in engineering applications for the last 10 years despite them having quite different views in principle. However, no comparison has been carried out to inform which method is actually better for engineering applications in terms of modal identification accuracy, uncertainty quantification, and so forth. This paper therefore investigates the differences between these two quantifications of parameter uncertainty in system identification. Synthetic data for a six-degreeof-freedom spring-mass numerical system are first studied to compare their identification accuracy and applicable conditions. The investigation is then extended to field data from actual structures, which involves ambient modal testing of Heritage Court Tower in Canada, Canton Tower in China, and Ting Kau Bridge in Hong Kong. Results from the synthetic data show that, under white-noise excitation, identified modal parameters and quantified uncertainty for both methods are highly consistent with the values of frequentist statistics. However, when the excitation is not white noise, there may be some spurious modes identified via SSI, and the uncertainty quantified under colored-noise excitation via SSI is almost always larger than those under white noise, while the Bayesian method is not disturbed. Results for the structural field data indicate that in general applications under environmental excitation the identified modal parameters from both methods are almost identical; the quantified uncertainty of the SSI method is slightly larger than that of the Bayesian method, but they are of the same order of magnitude and can meet engineering requirements.

Automated operational modal analysis based on long‐term records: A case study of Milad Tower structural health monitoring

“…The confidence interval of modal parameters with non‐normal distributions has been addressed using higher‐order moments than variance (i.e., skewness and kurtosis) 26 . The comparison between the confidence intervals of SSI and Bayesian methods has also been made, using the field data from Heritage Court Tower in Canada, Canton Tower in China, and Ting Kau Bridge in Hong Kong 27 . Most large‐scale structures are occasionally and partly excited under ambient loads (e.g., wind and traffic) concerning real‐world applications.…”

Constrained mode decomposition method for modal parameter identification

“…These classical methods use the relevant knowledge of structural vibration theory to establish a mathematical model of the relationship between the vibration response and modal parameters, 15,16 which is then used to identify and possibly optimize the modal parameters. Such classical approaches are characterized by a clear mechanical meaning of the procedures and a usually high identification accuracy 17 …”

“…Such classical approaches are characterized by a clear mechanical meaning of the procedures and a usually high identification accuracy. 17 The existing mode decomposition methods operate usually in two steps: mode decomposition and modal identification. Various signal processing techniques are first employed to decompose the recorded response signal into multiple responses that are presumed to be single mode.…”

A streamline approach to multiaxial fatigue monitoring using virtual sensing

“…19 Further examples on the application of filter-type techniques can be found in the literature. [20][21][22][23][24][25][26][27][28][29] Substituting the KF with these successors can improve the accuracy of fatigue monitoring methodology. Recently, the DKF has been applied to estimate the system state with aims to compute fatigue damage accumulation.…”

“…A two‐step Bayesian approach was employed to track changes in the system properties and update the modal parameters in a joint input–state estimation algorithm to predict the system response 19 . Further examples on the application of filter‐type techniques can be found in the literature 20–29 . Substituting the KF with these successors can improve the accuracy of fatigue monitoring methodology.…”