This study applied the kriging model and particle swarm optimization (PSO) algorithm for the dynamic model updating of bridge structures using the higher vibration modes under large-amplitude initial conditions. After addressing the higher mode identification theory using time-domain operational modal analysis, the kriging model is then established based on Latin hypercube sampling and regression analysis. The kriging model performs as a surrogate model for a complex finite element model in order to predict analytical responses. An objective function is established to express the relative difference between analytically predicted responses and experimentally measured ones, and the initial finite element (FE) model is hereinafter updated using the PSO algorithm. The Jalón viaduct—a concrete continuous railway bridge—is applied to verify the proposed approach. The results show that the kriging model can accurately predict the responses and reduce computational time as well.

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Subspace-based algorithms for operational modal analysis have been extensively studied in the past decades. In the postprocessing of subspace-based algorithms, the stabilization diagram is often used to determine modal parameters. In this paper, an improved stabilization diagram is proposed for stochastic subspace identification. Specifically, first, a model order selection method based on singular entropy theory is proposed. The singular entropy increment is calculated from nonzero singular values of the output covariance matrix. The corresponding model order can be selected when the variation of singular entropy increment approaches to zero. Then, the stabilization diagram with confidence intervals which is established using the uncertainty of modal parameter is presented. Finally, a simulation example of a four-story structure and a full-scale cable-stayed footbridge application is employed to illustrate the improved stabilization diagram method. The study demonstrates that the model order can be reasonably determined by the proposed method. The stabilization diagram with confidence intervals can effectively remove the spurious modes.

SUMMARY In this study, the effects of high‐amplitude initial conditions on the accuracy of modal parameters, identified from output‐only vibration data, are investigated. The influence on the sample output correlation function, which is the basis of most time‐domain operational modal analysis techniques, is analyzed first. Then, a numerical simulation is performed to quantify the effect of nonzero initial conditions on the relative accuracy of natural frequencies, damping ratios, and mode shapes. It is shown that, when all identification assumptions are satisfied, high‐amplitude initial conditions can significantly reduce the estimation errors, especially for short data records. Finally, a full‐scale application is presented where the modal parameters of a six‐span high‐speed railway bridge are determined from output‐only data. The results obtained with two different data sets are compared: The first one consists of the bridge's response to ambient data only, whereas the second one also contains the free vibration recorded immediately after a train passage. Although for most modes the results are similar, it is possible to identify some additional bending and torsion modes from the free vibration data with good accuracy. Copyright © 2013 John Wiley & Sons, Ltd.

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