2024 : 11 : 21
Hooshang Dabbagh

Hooshang Dabbagh

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 56926457100
HIndex:
Faculty: Faculty of Engineering
Address: Department of Civil Engineering-University of Kurdistan-Gharadian-Pasdaran Blvd.-Sanandaj-Iran
Phone: (+98)8733662313

Research

Title
Reliability-based stochastic finite element using the explicit probability density function
Type
JournalPaper
Keywords
change-of-variable; perturbation; probability density function; reliability analysis; stochastic finite element
Year
2023
Journal Structural Engineering and Mechanics
DOI
Researchers Rezan Choobdarian ، Azad Yazdani ، Hooshang Dabbagh ، Mohammad Rashid Salimi

Abstract

This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.