Estimation of wind loads on a tall building by an inverse method

Abstract

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.

Time-Varying Wind Load Identification Based on Minimum-Variance Unbiased Estimation

“…To obtain the minimum-variance unbiased estimation of state vector, the gain matrixK Z, +1 is calculated by minimizing the state estimation error covariance P Z, +1| +1 under the unbiased condition shown in (20). The gain matrixK Z, +1 is given byK…”

“…Zhi et al developed the Kalman filter method to estimate the wind load on super-tall buildings with limited structural responses. The effects of the type of wind-induced response, the covariance matrix of noise, errors of structural modal parameters, and the number of vibration modes were investigated through a detailed parametric study [20,21]. Gillijns and Moor addressed simultaneously estimating the state and the unknown input method based on the minimumvariance unbiased estimation in 2007 [22,23] and to the best of the authors' knowledge it has not been applied to wind load identification.…”

Novel health monitoring technology for in‐service diagnostics of intake separation in aircraft engines

“…Wind is an important factor that needs to be taken in consideration. It creates excessive vibrations in tall buildings and other wind‐sensitive structures, thus increasing the risks of losing their structural integrity . In case of an aircraft engine, wind (especially the crosswind) modifies interaction of the airflow with engine inlet structures, where geometry of the intake defines parameters of the airflow ingested by the engine and resistance to flow separation at varying operational conditions …”

Wind Load and Structural Parameters Estimation from Incomplete Measurements

“…To address the aforementioned issue, Hwang et al proposed the Kalman filtering approach in modal domain to estimate modal loads on a structure using limited measured response [16,17]. Zhi et al developed the Kalman filtering method and proper orthogonal decomposition technique to estimate the modal wind load on tall buildings [18,19]. In 2007, Gillijns and Moor proposed an approach for joint input-state estimation in discrete-time dynamic system based on minimum variance unbiased solution [20].…”