This article presents a Kalman‐filter‐based estimation algorithm for identification of wind loads on a super‐tall building using limited structural responses. In practice, acceleration responses are most convenient to be measured among wind‐induced dynamic responses of structures. The proposed inverse method allows estimating the unknown wind loads and structural responses of a super‐tall building using limited acceleration measurements. Taipei 101 Tower is a super‐tall building with 101 stories and a height of 508 m. Field measurements and numerical simulations of the wind effects on Taipei 101 Tower are conducted. The wind loads acting on the super‐tall building are estimated based on the wind‐induced responses determined from the numerical simulations and the refined finite‐element model of the structure, which are in good agreement with the exact results. The stability performance of the proposed algorithm is evaluated. The influence of noise levels in the measurements and covariance matrix of noise on the identification accuracy are investigated and discussed based on the L‐curve method. Finally, the wind loads and structural responses are reconstructed based on the field‐measured accelerations during Typhoon Matsa. The accuracy of the identified results is verified by comparing the reconstructed acceleration responses with the field measurements. The results of this study show that the proposed inverse approach can provide accurate predictions of the wind loads and wind‐induced responses of super‐tall buildings based on limited measured responses.

Many engineering applications require the knowledge of wind loads on structures. However, it is difficult or even impossible to measure these excitation forces from prototype structures directly. In this paper, a Kalman filteringbased inverse approach is developed to estimate the wind loads on tall buildings. The inverse method allows estimating the wind forces on a tall building based on limited structural responses. The optimum solution of Kalman filter gain by solving the Riccati equation is used to update the wind load identification. The practicability and accuracy of the developed inverse method are evaluated based on wind tunnel testing results of a squareshaped tall building. The wind loads identified by the developed method are compared with those by an augmented Kalman filtering-based technique for further verification of the effectiveness and reliability of the presented inverse approach. The influences of key factors such as the type of wind-induced response, covariance matrix of external loads, covariance matrix of noise, errors of structural modal parameters, and levels of noise involved in the measured responses on the wind load estimations are examined and discussed. It is shown through the comparative studies that the developed inverse method is an effective tool for estimating the wind loads on tall buildings based on limited structural responses.

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