Open AccessDynamic load identification of stochastic structures based on unscented transformation
Author(s) -
Chongwen Wang,
Chengbin Du
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012026
Subject(s) - discretization , mathematics , transformation (genetics) , monte carlo method , gaussian , convolution (computer science) , regularization (linguistics) , sigma , standard deviation , mathematical optimization , computer science , mathematical analysis , statistics , artificial neural network , biochemistry , chemistry , physics , quantum mechanics , machine learning , artificial intelligence , gene
In view of some unknown parameters in structures, which are described by the Gaussian distribution model in this paper, a new dynamic load identification method for stochastic structures is proposed based on unscented transformation (UT). Firstly, the convolution equation for solving structural response is discretized, and the sampling points, namely, sigma points of stochastic parameters are calculated according to the unscented transformation. Then, when the stochastic parameters take each sigma point, the dynamic load is calculated by the direct inversion method combined with the regularization method. Finally, the mean value and standard deviation of the identified load are obtained, and the coefficient of variation and the upper and low bounds of the identified load are defined. The result of an example shows that compared with the Monte Carlo simulation (MCS) and perturbation method (PM), the proposed method has higher computational efficiency and accuracy.