Identification of wind loads on super-tall buildings by Kalman filter

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.

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Wind Load and Structural Parameters Estimation from Incomplete Measurements

Load identification is a very important and challenging indirect load measurement method because load identification is an inverse problem solution with ill-conditioned characteristics. A new method of load identification is proposed here, in which a virtual function was introduced to establish integral structure equations of motion, and partial integration was applied to reduce the response types in the equations. The effects of loading duration, the type of basis function, and the number of basis function expansion items on the calculation efficiency and the accuracy of load identification were comprehensively taken into account. Numerical simulation and experimental results showed that our algorithm could not only effectively identify periodic and random loads, but there was also a trade-off between the calculation efficiency and identification accuracy. Additionally, our algorithm can improve the ill-conditionedness of the solution of load identification equations, has better robustness to noise, and has high computational efficiency.

Study on an Integral Algorithm of Load Identification Based on Displacement Response.

A dynamic stiffness-based framework for harmonic input estimation and response reconstruction considering damage

Wind loads on structures are difficult to directly measure, so it is practical to identify structural wind loads based on the measurements of structural responses. However, this inversed problem is challenging compared with conventional load identification as wind loads are time-space coupled and spatially distributed dynamic loads on structures. An improved method is proposed for identifying wind loads on structures using only partial measurements of structural acceleration responses in this paper. First, the wind loads on a structure are decomposed by proper orthogonal decomposition as a series of time-space decoupled sub-distributed dynamic loads with independent basic spatial distribution functions and time history functions. Herein, structural modes are adopted as the basic spatial distribution functions and structural modes of discretized and continuous structural systems are investigated. Then, a history function of the decomposed wind load is identified in the modal domain based on modal Kalman filter with unknown inputs, which is proposed by the authors. Finally, the distributed wind loads are reconstructed for discrete or continuous structural systems. The feasibility of the proposed algorithm is verified by two numerical examples of identification of wind loads on a discrete shear frame and a wind turbine tower, respectively.

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Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

A Kalman filtering–based inverse method for identification of wind forces on supertall buildings is proposed. The new approach allows simultaneously estimating the wind loads and entire wind-induced responses of a supertall building using only a limited set of measurements. The effectiveness and good accuracy of the proposed algorithm are validated with experimental results. The wind loads acting on a supertall building are estimated based on the wind-induced responses determined from wind-tunnel testing. The stability of the proposed algorithm is evaluated. The effects of several key factors such as measurement noise, errors of structural modal parameters, and covariance matrix of noise on the identification accuracy are examined and discussed using the L-curve method. Finally, the identified wind loads on the supertall building based on field-measured displacements were compared with the wind-tunnel testing results to further verify the effectiveness of the inverse technique.

Jun Yi

Papers

Identification of Wind Loads on Supertall Buildings Using Kalman Filtering–Based Inverse Method