Updated Bayesian detection of foundation parameter with Jeeves pattern search theory

Updated Bayesian detection of foundation parameters in the specific foundation mechanical model was studied based on Jeeves pattern search theory. Firstly, the updated Bayesian objective function for general foundation parameters was derived which could synchronously take the stochastic property of...

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Bibliographic Details
Published in:Journal of Zhejiang University. A. Science 2018-09, Vol.19 (9), p.691-703
Main Authors: Zhang, Jian, Jia, Chao, Zhou, Chu-wei
Format: Article
Language:English
Subjects:
Bayesian analysis
Civil Engineering
Classical and Continuum Physics
Convergence
Differential equations
Engineering
Filtration
Fourier transforms
Industrial Chemistry/Chemical Engineering
Interpolation
Iterative methods
Kalman filters
Mathematical models
Mechanical Engineering
Mindlin plates
Numerical stability
Objective function
Pattern search
Plate theory
Process parameters
Searching
Stochasticity
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Summary:Updated Bayesian detection of foundation parameters in the specific foundation mechanical model was studied based on Jeeves pattern search theory. Firstly, the updated Bayesian objective function for general foundation parameters was derived which could synchronously take the stochastic property of systematic parameters and systematic responses into account. Then the governing differential equations for the Winkler foundation model were gained with elastic Mindlin plate theory and the Fourier close form solution of the foundation model was achieved with the Fourier transform method. After the step length of pattern movement was determined with the quadratic parabolic interpolation method, the updated Bayesian detection of stochastic foundation parameters was resolved with Jeeves pattern search theory and then the corresponding detection procedure was completed. Through particular example analysis, the updated Bayesian detection of stochastic foundation parameters has excellent numerical stability and convergence during iterative processes. Jeeves pattern search theory is unconcerned with the partial derivatives of systematic responses to foundation parameters, and undoubtedly has satisfactory iterative efficiency compared with the available Kalman filtering or conjugate gradient detections of the significant foundation parameters. If the iterative processes are efficiently convergent, it is an important prerequisite that the systematic response assignment should be accurate enough. The derived Jeeves pattern search method with updated Bayesian theory can be applied in other kinds of foundation parameters.
ISSN:1673-565X
1862-1775
DOI:10.1631/jzus.A1700573