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

Abstract

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.

Wind Load and Structural Parameters Estimation from Incomplete Measurements

“…Figure 2 shows that the simulated power spectral density matches very well with the Davenport spectral. e wind load acting on the building structure is calculated according to [22]. e air density is assumed to be ρ � 1.29 kg/m 3 .…”

“…is method requires no assumption or prior knowledge about the unknown inputs, and it can be used for wind load identification in the physical domain [22]. Unfortunately, the aforementioned wind load estimation approaches assumed that the structural parameters are known a priori.…”

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

“…Wu et al [15] proposed a new wind load inversion method, which decomposed wind load into the product of orthogonal basis of position and time history function and took fully observed structural displacements, derived velocity and acceleration responses to obtain the wind load of the structure by least square estimation. Xue et al [16,17] used the method of unbiased estimation to identify wind load under both known and unknown structural parameters. In a case study of the 1310 m long Hardanger Bridge, Petersen et al [18,19] studied wind loads in time domain and frequency domain and introduced the Potential Gaussian Process model (GP-LFMS) to characterize the evolution of wind loads.…”

“…When the modal wind load is transformed into the physical coordinate wind load, the modal shape is no longer a square array after truncation. In some studies [16,17] the pseudo-inverse of the modal matrix was calculated and the modal wind loads were converted to wind loads in physical coordinates, but the use of the pseudo-inverse resulted in non-unique results. How to avoid the pseudo-inverse of the matrix is also content to be studied.…”

A Comparative Study of Regularization Method in Structure Load Identification

“…In the past two decades, a large number of researchers have carried out many effective research studies on inverse problems such as structural load identification and damage identification [1][2][3][4][5][6], and many of the results have been applied to the structural health monitoring technology of structural integrity and safety assessment [7][8][9][10]. Most of the load identification techniques use these regularization methods, such as the Tikhonov method and the truncated singular value decomposition (TSVD) method.…”