Finite element model updating for the Tsing Ma Bridge tower based on surrogate models

This paper investigates the efficiency of three surrogate model-based dynamic finite element model updating methods, the response surface, the support vector regression, and the radial basis function neural network, using the engineering background of the Tsing Ma Bridge tower. The influences of two different sampling methods (central composite design sampling and Box–Behnken design sampling) on the model updating results are also assessed. It was deduced that the impact of the surrogate model type on the updating results is not significant. More precisely, the models updated using the response surface method and the support vector regression method are similar in terms of reproducing the dynamic characteristics of the physical truth. However, the effects of the employed sampling method on the model updating results are significant as the model updating quality using the central composite design sampling method is higher than that using the Box–Behnken design sampling method in some considered cases.

Numerical predictions of wind-induced buffeting vibration for structures by a developed pseudo-excitation method

A numerical analysis method for wind-induced response of structures is presented which is based on the pseudo-excitation method to significantly reduce the computational complexity while preserving accuracy. Original pseudo-excitation method was developed suitable for adoption by combining an effective computational fluid dynamic method which can be used to replace wind tunnel tests when finding important aerodynamic parameters. Two problems investigated are gust responses of a composite wing and buffeting vibration responses of the Tsing Ma Bridge. Atmospheric turbulence effects are modeled by either k–ω shear stress transport or detached eddy simulation. The power spectral responses and variances of the wing are computed by employing the Dryden atmospheric turbulence spectrum and the computed values of the local stress standard deviation of the Tsing Ma Bridge are compared with experimental values. The simulation results demonstrate that the proposed method can provide highly efficient numerical analysis of two kinds of wind-induced responses of structures and hence has significant benefits for wind-induced vibration engineering.

ADVANCED FINITE ELEMENT MODEL OF TSING MA BRIDGE FOR STRUCTURAL HEALTH MONITORING

The Tsing Ma Bridge is a cable suspension bridge carrying both highway and railway. A bridge health monitoring system called wind and structural health monitoring system (WASHMS) has been installed in the Tsing Ma Bridge and operated since 1997 to monitor the structural performance and its associated loads and environments. However, there exists a possibility that the worst structural conditions may not be directly monitored due to the limited number of sensors and the complexity of structure and loading conditions. Therefore, it is an essential task to establish structural performance relationships between the critical locations/components of the bridge and those instrumented by the WASHMS. Meanwhile, to develop and validate practical and effective structural damage detection techniques and safety evaluation strategies, the conventional modeling for cable-supported bridges by approximating the bridge deck as continuous beams or grids is not applicable for simulation of real damage scenarios. To fulfil these tasks, a detailed full three-dimensional (3D) finite element model of the Tsing Ma Bridge is currently established for direct computation of the stress/strain states for all important bridge components. This paper presents the details of establishing this full 3D finite element model and its calibration. The major structural components are modeled in detail and the connections and boundary conditions are modeled properly, which results in about half million elements for the complete bridge model. The calibration of vibration modes and stresses/strains due to passing trains is carried out, and a good agreement is found between the computed and measured results.