Accumulative Bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theory
With general Bayesian theory, the accumulative Bayesian objective function of displacement constants of a hybrid indeterminate box girder was found. The gradient matrix of accumulative Bayesian objective function to displacement constants and the calculative covariance matrix were both derived. The...
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doaj-a021f179ffaa4bc18462a03c39063e002020-11-25T03:44:32ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402019-02-011110.1177/1687814018824164Accumulative Bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theoryJian Zhang0Chao Jia1Chuwei Zhou2Department of Mechanics and Structural Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaInstitute of Marine Science and Technology, Shandong University, Qingdao, ChinaDepartment of Mechanics and Structural Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaWith general Bayesian theory, the accumulative Bayesian objective function of displacement constants of a hybrid indeterminate box girder was found. The gradient matrix of accumulative Bayesian objective function to displacement constants and the calculative covariance matrix were both derived. The finite curvilinear strip controlling equation of a pinned box girder was derived and the hybrid indeterminate problem of a continuous curvilinear box girder with diaphragm was solved based on agglomeration theory. Combined with one-dimensional (1D) Fibonacci automatic search scheme of optimal step length, the variable scale gradient theory was utilized to research the stochastic detection of displacement constants of the hybrid indeterminate curvilinear box girder. Then the detection steps of displacement constants of the hybrid indeterminate curvilinear box girder were presented in detail and the detection procedure was developed. Through some classic examples, it is achieved that the accumulative Bayesian detection of displacement constants of the hybrid indeterminate curvilinear box girder has perfect numerical stability and convergence, which demonstrates that the derived detection model is correct and reliable. The stochastic performances of displacement constants and structural responses are simultaneously deliberated in an accumulative Bayesian objective function, which proves to have high computational efficiency. The variable scale gradient method incessantly changes the spatial matrix scale to engender new search directions during the iterative processes, which makes the derived accumulative Bayesian detection of the displacement constants more efficient.https://doi.org/10.1177/1687814018824164 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jian Zhang Chao Jia Chuwei Zhou |
spellingShingle |
Jian Zhang Chao Jia Chuwei Zhou Accumulative Bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theory Advances in Mechanical Engineering |
author_facet |
Jian Zhang Chao Jia Chuwei Zhou |
author_sort |
Jian Zhang |
title |
Accumulative Bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theory |
title_short |
Accumulative Bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theory |
title_full |
Accumulative Bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theory |
title_fullStr |
Accumulative Bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theory |
title_full_unstemmed |
Accumulative Bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theory |
title_sort |
accumulative bayesian detection of displacement constants of a hybrid indeterminate box girder with variable scale gradient theory |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2019-02-01 |
description |
With general Bayesian theory, the accumulative Bayesian objective function of displacement constants of a hybrid indeterminate box girder was found. The gradient matrix of accumulative Bayesian objective function to displacement constants and the calculative covariance matrix were both derived. The finite curvilinear strip controlling equation of a pinned box girder was derived and the hybrid indeterminate problem of a continuous curvilinear box girder with diaphragm was solved based on agglomeration theory. Combined with one-dimensional (1D) Fibonacci automatic search scheme of optimal step length, the variable scale gradient theory was utilized to research the stochastic detection of displacement constants of the hybrid indeterminate curvilinear box girder. Then the detection steps of displacement constants of the hybrid indeterminate curvilinear box girder were presented in detail and the detection procedure was developed. Through some classic examples, it is achieved that the accumulative Bayesian detection of displacement constants of the hybrid indeterminate curvilinear box girder has perfect numerical stability and convergence, which demonstrates that the derived detection model is correct and reliable. The stochastic performances of displacement constants and structural responses are simultaneously deliberated in an accumulative Bayesian objective function, which proves to have high computational efficiency. The variable scale gradient method incessantly changes the spatial matrix scale to engender new search directions during the iterative processes, which makes the derived accumulative Bayesian detection of the displacement constants more efficient. |
url |
https://doi.org/10.1177/1687814018824164 |
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