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.

Computational cost reduction and the best solution seeking are frequently encountered during model updating for complex structures. In this study, a hybrid algorithm using kriging model and genetic algorithms (GAs) is proposed for updating the Finite Element (FE) model of complex bridge structures employing both static and dynamic experimental measurements. The kriging model is first established to approximate the implicit relationship between structural parameters and responses, serving as a surrogate model for complex FE model when deriving analytical responses. An objective function is later defined based on the residual between analytical response values and experimental measured ones. GAs are finally employed to find the best solution by searching on the whole design space of updating parameters selected based on a sensitivity analysis. To verify the proposed algorithm, Caiyuanba Yangtze River Bridge, a double decked of roadway and light railway bridge with a main span of 420 m is used. Both frequencies and displacements predicted by the updated model are more close to experimental measured ones. The results show that the kriging surrogate model has good accuracy in predicting response and can be used as a surrogate model to reduce computational cost, and GAs provide a higher chance to obtain global best solution.

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.

The traditional objective function (TOF) of bridge model updating is usually defined on the basis of the structural response residuals between numerically calculated and experimentally measured values. However, this model finds it difficult to determine the weighting factors, and the updated model lacks accuracy. To offset these disadvantages, this study proposes a novel objective function (NOF) for finite-element model updating of a long-span cable-stayed bridge using in situ static and dynamic measurements. The proposed NOF comprises the residual functions of the displacement assurance criterion, the position of the neutral axis, and the natural frequency. The global sensitivity analysis–based model updating uses the Kriging model as the surrogate model to save computational costs. The nondominated sorting genetic algorithm II is employed to find the Pareto solutions. The updating results with the TOF defined directly using displacement, stress, and frequency are listed for the purposes of comparison. The results show that the NOF avoids determining the weighting factors and provides more accurate results than the TOF. The updated displacement curves, the position of the neutral axis, and the frequencies obtained from the NOF are all closer to the experimentally measured ones than those obtained from the TOF. After model updating, the displacement’s relative errors are around 5% without considering the displacement values under 10 mm. The relative errors of the first four frequencies are all less than 10%. The updated model can serve as the baseline finite-element model of the bridge.