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Intercorrelated random fields with bounds and the Bayesian identification of their parameters: Application to linear elastic struts and fibers
Author(s) -
Rappel Hussein,
Girolami Mark,
Beex Lars A.A.
Publication year - 2022
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.6974
Subject(s) - random field , identification (biology) , string (physics) , beam (structure) , probabilistic logic , bounded function , bayes' theorem , field (mathematics) , uncertainty quantification , bayesian probability , computer science , algorithm , structural engineering , mathematics , statistical physics , engineering , mathematical analysis , physics , artificial intelligence , machine learning , statistics , botany , pure mathematics , mathematical physics , biology
Many materials and structures consist of numerous slender struts or fibers. Due to the manufacturing processes of different types of struts and the growth processes of natural fibers, their mechanical response frequently fluctuates from strut to strut, as well as locally within each strut. In associated mechanical models each strut is often represented by a string of beam elements, since the use of conventional three‐dimensional finite elements renders the simulations computationally inefficient. The parameter input fields of each string of beam elements are ideally such that the local fluctuations and fluctuations between individual strings of beam elements are accurately captured. The goal of this study is to capture these fluctuations in several intercorrelated bounded random fields. Two formulations to describe the intercorrelations between each random field, as well as the case without any intercorrelation, are investigated. As only a few sets of input fields are available (due to time constraints of the supposed experimental techniques), the identification of the random fields' parameters involves substantial uncertainties. A probabilistic identification approach based on Bayes' theorem is employed to treat the involved uncertainties.