The extended minimum variance unbiased estimation approach can be used for joint state/parameter/input estimation based on the measured structural responses. However, it is necessary to measure the structural displacement and acceleration responses at each story for the simultaneous identification of structural parameters and unknown wind load. A novel method of identifying structural state, parameters, and unknown wind load from incomplete measurements is proposed. The estimation is performed in a modal extended minimum variance unbiased manner, based on incomplete measurements of wind-induced structural displacement and acceleration responses. The feasibility and accuracy of the proposed method are numerically validated by identifying the wind load and structural parameters on a ten-story shear building structure with incomplete measurements. The effects of crucial factors, including sampling duration and the number of measurements, are discussed. Furthermore, the practical application of the developed inverse method is evaluated based on wind tunnel testing results of a 234 m tall building structure. The results indicate that the structural state, parameters, and unknown wind load can be identified accurately using the proposed approach.
In this study, the multiscale synthetic eddy method, which can establish coherent turbulent structures and satisfy predefined turbulent statistical and spectral properties, is employed to generate the inflow turbulence for large-eddy simulation of a high-rise building. The recycling method of Lund and synthetic eddy method is also applied to assess the suitability of multiscale synthetic eddy method. The wind pressure at each mesh face centre on the surface of the high-rise building model is exported in the simulation to determine the wind-induced aerodynamic loads. Compared with the synthetic eddy method, the multiscale synthetic eddy method result is in higher agreement with that of the recycling method of Lund in terms of the wind pressure distribution, wind load characteristic and external flow field of the high-rise building.
A minimum-variance unbiased estimation method is developed to identify the time-varying wind load from measured responses. The formula derivation of recursive identification equations is obtained in state space. The new approach can simultaneously estimate the entire wind load and the unknown structural responses only with limited measurement of structural acceleration response. The fluctuating wind speed process is investigated by the autoregressive (AR) model method in time series analysis. The accuracy and feasibility of the inverse approach are numerically investigated by identifying the wind load on a twenty-story shear building structure. The influences of the number and location of accelerometers are examined and discussed. In order to study the stability of the proposed method, the effects of the errors in crucial factors such as natural frequency and damping ratio are discussed through detailed parametric analysis. It can be found from the identification results that the proposed method can identify the wind load from limited measurement of acceleration responses with good accuracy and stability, indicating that it is an effective approach for estimating wind load on building structures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.